RetractedArticle

RETRACTED: Social networks of author–coauthor relationships

Authors:
If you want to read the PDF, try requesting it from the authors.

Abstract

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy).This article has been retracted at the request of the Editor-in-Chief and co-Editors, as it contain portions of other authors' writings on the same topic in other publications, without sufficient attribution to these earlier works being given. The principal authors of the paper acknowledged that text from background sources was mistakenly used in the Introduction without proper reference to the original source. Specifically, the first page and a half of the article (pp. 2177–2178) contain together excerpts from Wikipedia (first paragraph), Wasserman and Faust's “Social Network Analysis: Methods and Applications” (pp. 17–20) ISBN 10: 0-521-38707-8; ISBN 13: 978-0-521-38707-1. Publication Date: 1994, and W. de Nooy, A. Mrvar and V. Bategelj's “Exploratory Social Network Analysis with Pajek"” (pp. 31, 36, 123, and 133) ISBN 10: 0-521-60262-9; ISBN 13: 978-0-521-60262-4. Publication Date: 2005.The scientific community takes a strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. The re-use of material, without appropriate reference, even if not known to the authors at the time of submission, breaches our publishing policies.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Scientific social network analysis (Yang et al., 2009;Said et al., 2008) seeks to discover global patterns in the network of researchers working in a particular field. Common approaches uses bibliographic/scholarly data as the basis for this analysis. ...
... Scientific social network analysis has become a growing area in recent years ((Yang et al., 2009;Robardet and Fleury, 2009;Said et al., 2008) just to name a few in recent studies). Its goal is to provide a deeper insight into a research field or into the personal connections among fields by analysing relationships among researchers. ...
Conference Paper
Full-text available
Our paper discusses the potential use of Web Content Mining techniques for gathering scientific social information from the homepages of researchers. We will introduce our system which seeks [affiliation, position, start year, end year] information tuples on these homepages along with preliminary experimental results. We believe that the lessons learnt from these experiments may be useful for further scientific social web mining.
... Many of the issues in this research appear from the low degree of structure of the data as well as the ambiguities often present 9,22,27 . Another important issue is the analysis of the networks themselves 14,17,20,28 . ...
Article
Full-text available
The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform.
... In collaboration network a node represents a research professionals and link between two nodes represents that those authors are work together. SNA provides both visual and mathematical analysis of networks [18,24]. ...
Conference Paper
The importance of an actor in the network is measured by the different type of centrality metrics of Social Network Analysis (SNA). In the research community, who are the most prominent author or key on the network is the major discussion or research issue. Different types of centrality measures and citation based indices are available, but their result is varied from network to network. In this paper, we form a network of author and its co-author based on Maximum Spanning Tree and find out the key author based on social network analysis metrics like degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. After that we compare the result of all centrality measures of MST based network and original network, betweenness centrality value increases and the other centrality value decreases. Finally, we conclude that the betweenness centrality is useful to analyze key author in this type of network.
... In this paper, we seek to demonstrate that author-coauthor networks in the statistical literature satisfy these two criteria. There has been work on author-coauthor networks and the emergence of global brain in [2], and preferential attachment in [12], and citation networks in [8], and visualization of coauthor networks in [10], and consumer behavior research in [7], and implications for peer review in [13] . Coauthorship relationships can be treated as a 2-mode networks in which there are two types of nodes; the author nodes and paper nodes, and one relationship type; " person A authored/coauthored paper P " . ...
Chapter
Full-text available
In this paper, we suggest a model of preferential attachment in coauthorship social networks. The process of one actor attaching to another actor (author) and strengthening the tie over time is a stochastic random process based on the distributions of tie-strength and clique size among actors. We will use empirical data to obtain the distributions. The proposed model will be utilized to predict emerging scientific subfields by observing the evolution of the coauthorship network over time. Further, we will examine the distribution of tie-strength of some prominent scholars to investigate the style of coauthorship. Finally, we present an example of a simulated coauthorship network generated randomly to compare with a real-world network.
... The algorithm iteratively removes the edge with the highest betweenness, so to produce a hierarchical decomposition of the graph, where the remaining disconnected components are the communities discovered. Betweenness centrality has also been used to analyze social networks [22, 27, 31, 34], protein networks [21], wireless ad-hoc networks [29] , to study the importance and activity of nodes in mobile phone call networks [12] and interaction patterns of players on massively multiplayer online games [1], to study online expertise sharing communities of physicians [19], to analyze linking behavior of key bloggers in dynamic networks of blog posts [28], to inform the design of socially-aware P2P systems [24] , and to measure communication networks traffic [36], to name a few. Measuring betweenness centrality requires computing the shortest paths between all pairs of vertices in a graph. ...
Article
Full-text available
Betweenness centrality is a classical measure that quantifies the importance of a graph element (vertex or edge) according to the fraction of shortest paths passing through it. This measure is notoriously expensive to compute, and the best known algorithm runs in O(nm) time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper we propose the first truly scalable algorithm for the online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges are removed. Our algorithm is carefully engineered with out-of-core techniques and it is tailored for modern parallel stream processing engines that run on clusters of shared-nothing commodity hardware. Hence, it is amenable to real-world deployment. Our experiments confirm that, in graphs with millions of vertices and edges, our method is able to keep the betweenness centrality measures up to date online, i.e., the time to update the measures is smaller than the inter-arrival time between two consecutive edges.
... A network is highly centralized if there is a clear boundary between the center and the periphery. In a highly centralized network, information spreads easily, but the center is indispensable for the transmission of information " (Said, et al, 2008). " The status of an actor is usually expressed in terms of its centrality, i.e., a measure of how central the actor is to the network graph. ...
Article
Full-text available
This research examines the association between co-authorship network centrality (degree, closeness, betweenness, eigenvector, Bonacich flow betweenness) and productivity of Information science researchers. The research population includes all those researchers who have published at least one record in one of the twenty journals of Information Science which has an impact factor of 0.635 as a minimum from the years 1996 to 2010. By using social network analyses, this study examines information science researchers’ outputs during 1996-2011 in ISI Web of Science database. In general co-authorship network of these researchers was analyzed by UCINET6 software. Results showed that there is a significant correlation between Journal Impact Factor (JIF) and all centrality measures except closeness centrality at P= 0.001. Results also showed that there is a significant correlation between productivity of authors and all centrality measures scores at P≥ 0.001. Also, regression reports direct relationship of degree, closeness and flow betweenness and inverse relationship of betweenness as well as Eigen vector centrality on productivity of researchers. Keywords: Co-authorship; Network centrality; Scientific productivity; Social network analysis, Journal Impact Factor
... The idea of cumulative advantage implies that excellent scientists are rewarded far more than others in their field. Said et al. (2008) noted one factor affecting co-authorship ties is the mentor-student relationship: young researchers are more likely to form new co-authorship ties with older, established researchers, usually their mentors. The formal modelling of cumulative advantage in terms of preferential attachment as the driving mechanism of co-authorship was examined by Barabási and Albert (1999) who studied a common property of many large networks whose vertex degrees followed a scale-free power-law distribution. ...
Article
This paper examines the collaboration structures and dynamics of the co-authorship network of all Slovenian researchers. Its goal is to identify the key factors driving collaboration and the main differences in collaboration behavior across scientific fields and disciplines. Two approaches to modelling network dynamics are combined in this paper: the small-world model and the mechanism of preferential attachment, also known as the process of cumulative advantage. Stochastic-actor-based modelling of co-authorship network dynamics uses data for the complete longitudinal co-authorship networks for the entire Slovenian scientific community from 1996 to 2010. We confirmed the presence of clustering in all fields and disciplines. Preferential attachment is far more complex than a single global mechanism. There were two clear distinctions regarding collaboration within scientific fields and disciplines. One was that some fields had an internal national saturation inhibiting further collaboration. The second concerned the differential impact of collaboration with scientists from abroad on domestic collaboration. In the natural, technical, medical, and biotechnical sciences, this promotes collaboration within the Slovenian scientific community while in the social sciences and humanities this inhibits internal collaboration.
... Research on collaboration network was started with the pioneer work of Newman (2001). After that, a large number of research works have been conducted on the statistical analysis of collaboration network (Ding 2011;Kronegger et al. 2012;Martinez-Romo et al. 2008;Said et al. 2008) and modeling collaboration network through simulations (Huang et al. 2008;Liu et al. 2012;Tambayong 2007). Similarly, few related researches on collaboration network, namely developing author-ranking scheme through ''supportiveness'' analysis (Han et al. 2009), ego-centric network analysis of collaboration network (Abbasi et al. 2012), classifying personal names through collaboration network (Biryukov 2008), discovering the relationship between authors and research domains (Hassan and Ichise 2009), understanding and modeling diverse scientific careers of researchers (Chakraborty et al. 2015) etc. have been conducted. ...
Article
Full-text available
Collaboration networks are elegant representations for studying the dynamical processes that shape the scientific community. In this paper, we are particularly interested in studying the local context of a node in collaboration network that can help explain the behavior of an author as an individual within the group and a member along with the group. The best representation of such local contextual substructures in a collaboration network are “network motifs”. In particular, we propose two fundamental goodness measures of such a group represented by a motif—productivity and longevity. We observe that while 4-semi clique motif, quite strikingly, shows highest longevity, the productivity of the 4-star and the 4-clique motifs is the largest among all the motifs. Based on the productivity distribution of the motifs, we propose a predictive model that successfully classifies the highly cited authors from the rest. Further, we study the characteristic features of motifs and show how they are related with the two goodness measures. Building on these observations, finally we propose two supervised classification models to predict, early in a researcher’s career, how long the group where she belongs to will persist (longevity) and how much the group would be productive. Thus this empirical study sets the foundation principles of a recommendation system that would forecast how long lasting and productive a given collaboration could be in future.
... Authors who have a good closeness centrality are also significant, means an author is nearest to average no. of author. Authors who have a good betweenness centrality are also prominent and an author who have a good eigenvector centrality is also prominent in the network [35,36] means an author have a connection with a high score eigenvector centrality authors. Now, we calculate the normalized degree centrality, closeness centrality, betweenness centrality and eigenvector centrality for all researchers by using python and network [30]. ...
Chapter
Full-text available
Important author analysis is one of the key issues in the research professionals’ relationship network. Research professionals’ relationship network is a type of social network which is constitute of research professionals and there co-author relationship with other professionals. So many social network analysis metrics are available to analyze the important or prominent actor in the network. Centrality in social network analysis represents prestige or importance of a node with respect to other nodes in the network and also represents the importance of relationship between nodes. In this paper, we studied social network theory to understand how the collaboration of research professionals has impact in research world and performance of individual researcher. For this analysis, we use social network analysis metrics like normalize degree centrality, closeness centrality, betweenness centrality and eigenvector centrality.
... Several applications of blockmodeling of co-authorship networks have been published in recent years. For example, Said et al (2008) ...
Chapter
Full-text available
Scientific collaboration networks have been studied systematically since 1960 by scholars belonging to various disciplinary backgrounds. As a result, the complex phenomenon of scientific collaboration networks has been investigated within different approaches. Although the term “scientific collaboration network” has different connotations in the literature, we use the term more narrowly to focus on scientific collaboration resulting in co-authored public documents. We broaden this beyond journal articles to include many types of scientific productions in addition to journal articles and books. We insist that these productions are public items available in each field. In this chapter, we focus on the main quantitative approaches dealing with the structure and dynamics of scientific collaboration networks through co-authorized publications. We provide a brief history of social network analysis that serves as a foundation. We further review earlier conceptual classifications of co-authorship networks and distinguish cross-disciplinarily, cross-sectoral and cross-national levels. We couple the newer ideas of “small world” models and “preferential attachment” to older sociological conceptions of scientific collaboration. This is followed by descriptions of deterministic and stochastic models that have been used to study dynamic scientific collaboration networks. We stress the importance of delineating the topology of collaboration networks, understanding micro-level processes and then coupling them. We conclude by outlining the strengths and limitations of various modeling strategies.
... The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships [13] [17]. ...
Conference Paper
Full-text available
The centrality of vertices has been the key issue in social network analysis. Many centrality measures have been presented, such as degree, closeness, between's and eigenvector centrality. But eigenvector centrality is more suited than other centrality measures for finding prominent or key author in research professionals' relationship network. In this paper, we discuss eigenvector centrality and its application based on Network x. In eigenvector centrality first set every node a starting amount of influence then performs power iteration method. In network x the starting amount of influence of each node is 1/len(G). Therefore, we modify the eigenvector centrality algorithm and set the starting amount of influence of each node is the degree centrality of that node because eigenvector centrality is the extension of degree centrality and also implements the eigenvector centrality in weighted network.
... Based on these studies, models such as Random Graph [9], Scale Free [10], and Small Words [11] were proposed for real networks. In the second group, the studies have analyzed the individuals in the network, and the relationship between them, in order to use this information to improve tasks such as prediction, recommendation systems, and marketing campaigns [12, 13]. These studies focus, mainly, on investigating three basic assumptions: (1) the individual's behavior tends to be consistent over time; (2) the behavior of a group may explain individual behaviors; and (3) similar individuals tend to behave similarly. ...
Conference Paper
Full-text available
Complex networks are a popular and frequent tool for modeling a variety of entities and their relationships. Understanding these relationships and selecting which data will be used in their analysis is key to a proper characterization. Most of the current approaches consider all available information for analysis, aggregating it over time. In this work, we studied the impact of such aggregation while characterizing complex networks. We model four real complex networks using an extended graph model that enables us to quantify the impact of the information aggregation over time. We conclude that data aggregation may distort the characteristics of the underlying real-world network and must be performed carefully.
... PFNET and hierarchical clustering algorithms were used to analyze the co-citation matrices (4,5), and network analysis was used to analyze author cooperation relationships (6)(7)(8)(9). The results were incorporated into visualization maps using UCINET software. ...
Article
Objective: To investigate the output of evidence-based medicine (EBM) researchers in China and elsewhere by examining the EBM domains they work within and the networks that exist among them; using visualization methods to analyze these relationships. This maps the current situation and helps with the identification of areas for future growth. Methods: We used co-citation matrixes with Pathfinder networks and hierarchical clustering algorithms, and constructed a co-author matrix which were analyzed with a whole network approach. The analyzed matrixes were visualized with the UCINET program. Results: Much of the development of EBM has been centered around three authors, David Sackett, Gordon Guyatt and L Manchikanti, within three different clusters. The main authors of EBM articles in China were divided into nine academic domains. The relations among core authors of articles indexed by the Science Citation Index (SCI) was loose. There was a stronger co-authorship network among core authors in the Chinese literature, with three groups and 21 cliques. Nine distinct academic communities appeared to have formed around Li Youping, Liu Ming and Zhang Mingming. Conclusion: The EBM literature contains several key clusters, with universities in high-income countries being the source of the majority of articles. Outside China, McMaster University in Canada, the original home of EBM, is the dominant producer of EBM publications. In China, Sichuan University is the main source of EBM publications. The EBM cooperation network in China is comprised of three major groups, the largest and most productive in this sample is led by Li Youping with Liu Ming, Zhang Mingming, Li Jing, Wang Li, Wu Taixiang, and Liu Guanjian as central members.
... ing causal links to influence other entities behavior, such as genes in genomics or customers in marketing studies. Betweenness centrality has been used to analyze various social or general networks [13, 18, 19], to identify influential nodes surrounded by other influential nodes in social net- works [14] , and to measure network traffic in communication networks [21]. Node betweenness centrality, however, is computationally expensive. ...
Article
Full-text available
This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, κ-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high κ-path cen-trality have high node betweenness centrality. Experimen-tal evaluations on diverse real and synthetic social networks show improved accuracy in detecting high betweenness cen-trality nodes and significantly reduced execution time when compared to known randomized algorithms.
... Alan Mislove [11] addresses the issue of large scale measurement study and analysis of the structure of multiple online social networks. Co-author relation is represented in matrix form by [12] to cluster set of coauthors. User profile representation and personalized content retrieval proposed by [16] uses semantic clustering to identify user clusters. ...
Article
Full-text available
Social network is a group of individuals with diverse social interactions amongst them. The network is oflarge scale and distributed due to involvement of more people from different parts of the globe.Quantitative analysis of networks is need of the hour due to its’ rippling influence on the network dynamicsand in turn the society. Clustering helps us to group people with similar characteristics to analyze thedense social networks. We have considered similarity measures for statistical analysis of social network.When a social network is represented as a graph with members as nodes and their relation as edges, graphmining would be suitable for statistical analysis. We have chosen academic social networks and clusterednodes to simplify network analysis. The ontology of research interests is considered to measure similaritybetween unstructured data elements extracted from profile pages of members of an academic socialnetwork.
... Many of the issues in this research appear from the low degree of structure of the data as well as the ambiguities often present 9,22,27 . Another important issue is the analysis of the networks themselves 14,17,20,28 . ...
Article
Full-text available
The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.
... In collaboration network a node represents a research professionals and link between two nodes represents that those authors are work together. SNA provides both visual and mathematical analysis of networks [14,15]. ...
Chapter
In research community, who are the most prominent or key authors in the research community is the major discussion or research issue. Different types of centrality measures and citation based indices are developed for finding key author in community. But main issues is what are the real contribution of an individual or group and their impact in research community. To find contribution of individual researcher, we use normalized citation count and geometric series to distribute the share to individual author in multi-authored paper. For evaluating the scientific impact of individual researcher, we use eigenvector centrality. In eigenvector centrality first, we set the initial amount of influence of each author to total normalized citation score and the collaboration weight is correlation coefficient value.
... For example, the blockmodeling method has been used to explore the academic communities in the field of the library science (Liu et al. 2005). Said et al. used the blockmodeling methods to find the typical collaboration modes in the co-author network (Said et al. 2008). Graph partitioning was applied in the Exponential Random Graph Models (ERGMs) to delineate the author clusters in the co-author network (Zhang et al. 2016), thus studying the distance influence on the collaboration activities (Liang 2015). ...
Article
Full-text available
Academic community evolution reveals the development of scientific collaboration among scientists. These social interactions of researchers can be well reflected by co-author network, making it feasible to investigate academic community through looking into co-author network, and to study community evolution through dynamic co-author network analysis. Existing metrics measure an author’s impact or centrality in co-author network individually, rather than considering the academic community as a whole. Besides, co-authors of a paper usually make different contributions reflected in the name order, which is often ignored in traditional co-author network analysis. Furthermore, attention has been paid mainly on those structure-level characteristics like the small-world coefficient and the clustering coefficient, the content-level characteristics like community, author, and topics, however, are crucial in the understanding of community evolution. To address those problems, we firstly propose a “comprehensive impact index” to evaluate the author in a co-author network by comprehensively considering the statistic-based impact and the network-based centrality. Then the comprehensive index value of all authors in a community is added up to evaluate the community as a whole. Further, a lifecycle strategy is proposed for the community evolution analysis. Taking geography academic community as a pilot study, we select 919 co-authored papers from the flagship journal of “Annals of the Association of American Geographers”. The co-author groups are generated by community detection method. Top three co-author groups are identified through computing with the proposed index and analyzed through the proposed lifecycle strategy from perspective of community structures, member authors, and impacts respectively. The results demonstrate our proposed index and strategy are more efficient for analyzing academic community evolution than traditional methods.
... If we consider the collabo-ration network as social network, then node is the scholar and the edge is the collaboration between scholars. [15,16]. ...
... In the further research, some more effective and stable algorithm is given (Segarra and Ribeiro 2014). The algorithm has been widely used in complex networks research works such as analysis of social networks (Said et al. 2008), identifying critical nodes in a wireless network (Guo et al. 2014;Shen et al. 2015) and studied the activity and the importance of mobile telephone network nodes (Catanese et al. 2013). ...
Article
Full-text available
In online social networks, there are some influential opinion leader nodes who can be used to accelerate the spread of positive information and suppress the diffusion of rumors. If these opinion leaders can be identified timely and correctly, there will be contributing to guide the popular opinions. The closeness is introduced for mapping the relationship between the nodes according to the different interaction types in online social network. In order to measure the impact of the information transmission between non-adjacent nodes in online social networks, a closeness evaluating algorithm of the adjacent nodes and the non-adjacent nodes is given based on the relational features between users. By using the algorithm, the closeness between the adjacent nodes and the non-adjacent nodes can obtained depending on the interaction time of nodes and the delay of their hops. Furthermore, a more accurate and efficient betweenness centrality scheme based on the optimized algorithm with the degree of closeness and the corresponding updating strategy. The opinion leader nodes should be identified more accurately and efficiently under the improved algorithm because the considering of closeness between nodes in the network. Finally, the maximum spreading experiment is done for comparing the proposed method with other existing identifying opinion leader selecting schemes based on the Independent Cascade Model. The result of experiment shows the effectiveness and practicality of the evaluating algorithm.
Article
Full-text available
We combine two seemingly distinct perspectives regarding the modeling of network dynamics. One perspective is found in the work of physicists and mathematicians who formally introduced the small world model and the mechanism of preferential attachment. The other perspective is sociological and focuses on the process of cumulative advantage and considers the agency of individual actors in a network. We test hypotheses, based on work drawn from these perspectives, regarding the structure and dynamics of scientific collaboration networks. The data we use are for four scientific disciplines in the Slovene system of science. The results deal with the overall topology of these networks and specific processes that generate them. The two perspectives can be joined to mutual benefit. Within this combined approach, the presence of small-world structures was confirmed. However preferential attachment is far more complex than advocates of a single autonomous mechanism claim. KeywordsScientific collaboration–Co-authorship network–Bibliometry–Longitudinal network analysis–Small world–Preferential attachment–Stochastic actor based model
Chapter
With the ubiquitous characteristic of the Internet, today many online social environments are provided to connect people. Various social relationships are thus created, connected, and migrated from our real lives to the Internet environment from different social groups. Many social communities and relationships are also quickly constructed and connected via instant personal messengers, blogs, Twitter, Facebook, and a great variety of online social services. Since social network visualizations can structure the complex relationships between different groups of individuals or organizations, they are helpful to analyze the social activities and relationships of actors, particularly over a large number of nodes. Therefore, many studies and visualization tools have been investigated to present social networks with graph representations. In this chapter, we will first review the background of social network analysis and visualization methods, and then introduce various novel visualization applications for social networks. Finally, the challenges and the future development of visualizing online social networks are discussed.
Chapter
In this paper, I consider data on fatal automobile crashes, DWI arrests, and alcohol addiction admissions in Virginia, USA and use these as a basis for estimating the hourly, weekly, monthly, and annual cycles associated with alcohol consumption. In addition, I use surveys carried out by the Department of Alcoholic Beverage Control in Virginia to establish geospatial patterns of purchases of distilled spirits. This data analysis allows me to conjecture spatiotemporal patterns that can be incorporated into calibration of a more complex ecological alcohol systems model.
Article
Full-text available
Sturgeon species are among the commercially most valuable and the most endangered groups of fish. To assess the existing literature published within the field of sturgeon research over the past 15 years (1996–2010) we applied a bibliometric approach, in order to identify patterns and trends of the published research in this field. The analysis was performed based upon articles obtained from the ISI Web of Knowledge online database. The results revealed that although all 27 sturgeon species have been objects of the research, species that are endangered or facing a high probability of extinction have received disproportionately less attention. White sturgeon (Acipenser transmontanus) was the most frequently studied species, but it was recently surpassed by Persian sturgeon (A. persicus). Early life phases have been among the central objects of the research, and genetics, especially the use of microsatellite DNA, is becoming increasingly popular and had the highest impact. Research related to aquaculture was prominent, while the research related to hybrids (as a commodity of aquaculture production) was decreasing in popularity. Papers dealing with conservation issues were most frequently focused on European sturgeon (A. sturio). A steady increase in the number of published articles over time was observed. However, the overall citation rate declined significantly over time. During the period reviewed, the sturgeon research published in peer reviewed journals dominantly originated from the USA and EU. Nevertheless, considering the current trend in output, it is very likely that the Asian countries, mainly Iran and China, will surpass them within the next 5–10 years. International and inter-institutional collaboration both tended to increase the impact of the research. Stimulation and improvement of the international cooperation should be considered as future priorities.
Article
Strong ties play a crucial role in transmitting sensitive information in social networks, especially in the criminal justice domain. However, large social networks containing many entities and relations may also contain a large amount of noisy data. Thus, identifying strong ties accurately and efficiently within such a network poses a major challenge. This paper presents a novel approach to address the noise problem. We transform the original social network graph into a relation context-oriented edge-dual graph by adding new nodes to the original graph based on abstracting the relation contexts from the original edges (relations). Then we compute the local k-connectivity between two given nodes. This produces a measure of the robustness of the relations. To evaluate the correctness and the efficiency of this measure, we conducted an implementation of a system which integrated a total of 450GB of data from several different data sources. The discovered social network contains 4,906,460 nodes (individuals) and 211,403,212 edges. Our experiments are based on 700 co-offenders involved in robbery crimes. The experimental results show that most strong ties are formed with k⩾2.
Article
This article uses bibliometric analysis to empirically examine research on business ethics published in a broad set of journals, focused over the period 1988–2007. We consider those journals with an emphasis on accounting. First, we determine the citation frequencies of documents to identify the core articles in accounting research with an ethics focus as well as the contributions of influential fields included in the research sphere of these journals. We also employ document co-citation analysis to analyze the scholarly communication patterns that exist within the realm of the specified articles. Second, we utilize social network analysis tools to profile the centrality features of the co-citation network of these documents. Keywordscitation analysis-document co-citation analysis-social network analysis-accounting ethics
Conference Paper
Full-text available
3D technologies open up new possibilities for modeling business processes. They provide higher plasticity and eliminate some deficits of conventional 2D process modeling such as the limitation of the amount of information to be integrated into a process model in an understandable way. The aim of this paper is to show how the usage of an additional visual modeling dimension may support users in compactly representing and animating business process models. For this purpose, we propose an approach for 3D representation of business process models based on Petri nets. The need for the third modeling dimension is pointed out with three modeling scenarios for which we propose modeling improvements in 3D space. Early evaluations indicate the effectiveness of our approach, which goes beyond conventional modeling tools for business processes.
Conference Paper
Co-authorship in scientific research is increasing in the past decades. There are lots of researches focusing on the pattern of co-authorship by using social network analysis. However, most of them merely concentrated on the properties of graphs or networks rather than take the contribution of authors to publications and the semantic information of publications into consideration. In this paper, we employ a contribution index to weight word vectors generated from publications so as to represent authors' research interest, and try to explore the relationship between research interest and co-authorship. Result of curve estimation indicates that research interest couldn't be employed to predict co-authorship. Therefore,graph-based researcher recommendation needs further examination.
Article
Transboundary fish stocks are governed by multiple entities, involving individuals with different expertise and sociocultural backgrounds and representing various institutions and jurisdictions. At times, individuals from these entities collaboratively make fishery governance decisions, and the existence of collegial or personal relationships may facilitate the decision-making process and result in better management of fish stocks. Although studies have assessed several aspects of fisheries institutional structures, very few have looked at the impact of social network structure. In this study, we found evidence for the perceived effectiveness of A Joint Strategic Plan for Management of Great Lakes Fisheries in its social network structure. We focused on the frequency of interactions for exchanging information about Great Lakes fish stocks in general and lake sturgeon Acipenser fulvescens in particular. These informational exchanges correspond to distinct social network structures, the fish stocks network and the lake sturgeon network. Similarities in participants’ attributes and perceptions about fisheries governance may have facilitated the formation of social ties within these networks. The lake sturgeon network had significant subgroups wherein flow was more concentrated. The formation of these subgroups appears to have been influenced by individuals’ employer type, committee affiliation, and perception about lake sturgeon governance. These subgroups were found to have 11 central individuals and two central subgroups. When comparing the central and noncentral individuals’ attributes and perceptions, we did not find a significant difference that explains why the 11 individuals were central. Similarly, no significant differences were found in comparing the central and noncentral subgroups. In assessing the flow of information within and across these subgroups, we found that it was evenly distributed with some specialization of flow between subgroups, the latter probably related to the presence of bridging individuals connecting the subgroups.Received March 24, 2010; accepted March 23, 2011
Conference Paper
This study applies social network analysis to study the collaboration of scholars in the field of graduate education. The result shows that from 1980 to 2015, the percentage of co-authored papers remarkably increases and a growing number of core scholars appears in the the field of graduate education. The density of co-authorship network is low but is increasing. According to analysis on the patterns of subnets in the whole co-authorship network, the collaboration of scholars develop from single authorship pattern to complete and potential co-authorship pattern. Some core research groups have already formed in graduate education, their academic contributions greatly promote the development of graduate education.
Article
Full-text available
Bibliometric studies are considered very important in terms of determining the current status of scientific publications in the field and being a guide for researchers working in the field. In this study, bibliometric analysis was carried out in order to contribute to the field by revealing the general structure of the science curriculum area. For this purpose, 1716 studies published between the years 1970-2019 related to the science curriculum were examined from a bibliometric point of view, and trends and trends in this field were revealed. In the Web of Science Core Collection (WoS) database, the keyword of "Science Curriculum" was scanned and bibliometric data of the studies were obtained. The studies have been examined by the number of publications, publication types, publication languages, citation analysis, country collaborations, common citation networks and concept-subject orientations by years. The authors and works to which the examined studies are addressed are also examined within the scope of the study. Thus, it was ensured that important authors and works that were of great interest to researchers periodically were identified and the interactions between them were identified. In the study, country collaborations, cited journals, authors, publications and the positions of the concepts in the network were evaluated. As a result of the research, it was observed that there was a rapid increase in the number of publications in the field after 2010, and the most studies were conducted in article type and in English. However, it has been determined that the USA plays a key role in country partnerships and the journal with the most citation boom is School Science Review and the author is Rosalind Driver. Looking at the most studied subjects in the field, it was seen that the topics of student access and curriculum design came to the fore.
Article
Interinstitutional scientific collaboration plays an important role in knowledge production and scientific development. Together with the increasing scale of scientific collaboration, a few institutions that positively participate in interinstitutional scientific collaboration are important in collaboration networks. However, whether becoming an important institution in collaboration networks could be a contributing factor to research success and how these important institutions collaborate are still indistinct. In this paper, we identified the scientific institutions that possess the highest degree centrality as important institutions of an interinstitutional scientific collaboration network in materials science and examined their collaboration preferences utilizing several network measures. We first visualized the appearance of these important institutions that had the most positive collaborations in the interinstitutional scientific collaboration networks during the period of 2005–2015 and found an obvious scale-free feature in interinstitutional scientific collaboration networks. Then, we measured the advantages of being important in collaboration networks to research performance and found that positive interinstitutional collaborations can always bring both publication advantages and citation advantages. Finally, we identified two collaboration preferences of these important institutions in collaboration networks—one type of important institution represented by the Chinese Academy of Science plays an intermediary role between domestic institutions and foreign institutions with high betweenness centrality and a low clustering coefficient. This type of important institution has better performance in the number of publications. The other type of important institution represented by MIT tends to collaborate with similar institutions that have positive collaborations and possess a larger citation growth rate. Our finding can provide a better understanding of important institutions’ collaboration preferences and have significant reference for government policy and institutional collaboration strategies.
Chapter
This chapter discusses ethical aspects of network analysis and the connection to network analysis literacy.
Article
How close two entities are in social network is a key factor of SNA (Social Network Analysis). Recent studies of social networks contain a large number of entities and huge number of relations/connections in the networks. Efficiently and accurately analyzing relationships in the network is important component of SNA, especially for law enforcement. In this paper we propose using the edge-dual graph to transform the traditional social network graph to a relation context oriented graph and using modified k-connectivity concepts to evaluate the robustness of the relations. We also describe an implementation of a system based on a 450GB data source, involving 5 million people in Alabama. We use this large scale implementation to evaluate the performance and correctness of the proposal. Our evaluation suggests that using this relation context oriented technology will help to construct a more accurate social network.
Article
The main objective of this study is to investigate the intellectual structure and evolution of author collaborations from articles published in the Strategic Management Journal between 1980 and 2014. This assessment includes the general view of authorship, authorship patterns, author productivity, ranking of authors, visualization of the co-authorship network, comparison of strategic management co-authorship network attributes with those of other disciplines, the evolution of main components and core authors in the networks by period, discussions on whether the strategic management network fits with the small world network theory, individual network attributes such as degree centrality, Bonacich's power index, closeness centrality, and betweenness centrality. Finally, the authors provide an inclusive evaluation of the results, limitations, and suggestions for future research.
Conference Paper
Full-text available
In social network analysis, the importance of an actor can be found by using the centrality metrics. There are many centrality metrics available e.g. degree, closeness, betweenness, eigenvector etc. In research community authors forms a social network, which is called Research Professionals’ Collaboration Network. This is similar to social network where each author is an actor and an article written together by some authors establishes collaboration between them. Each author acquires a certain value based on the citation of their articles. There are many citation indices are available such as citation count, h-index, g-index, i10-index etc. To analyze the Research Professionals’ collaboration Network and for finding the key author, the citation indices can be used. In this paper, we compare and combine both social network analysis metrics and the citation indices to get better result in finding the key author.
Article
Full-text available
The article deals with some processes generating increases in research collaboration; one of the most characteristic tendencies of modern science. The major empirical focus is the increasing tendency to co-authorship in sociological publications in Slovenia. Bibliometric analyses, based on two joint national research information systems (SICRIS and COBISS), show the amount of coauthored publications in the field of sociology have increased over the last two decades. Blockmodeling of co-authorship networks in sociology has shown that sociologists who are not systematically tied to strongly connected and wellestablished research groups produce the best scientific publications in their field.
Article
Scientific collaboration or co-authorship has different forms and can be a factor in creating knowledge and even increasing the quality of scientific works. Beyond the quantity, qualitative factors also affect scientific collaboration. Two factors Collaboration intensity and member diversity can predict research quality, so teams with constant or diverse collaborators could affect co-authorship network’s quality. Current study used scientometrics methods including SNA. Research data were “information literacy” related documents indexed in Scopus database, during years 1941–2019. Scopus.exe, UCINET 6 and NetDraw were used for analyzing data. Results show that ϕ-index has a negative relationship with number of co-authors, degree and ties. This means that the higher number of co-authors, degree and ties the lower ϕ-index, which is confirms ϕ-index meaning. Centrality betweenness has a positive relationship with the number of articles, co-authors and ties which means that betweenness of authors goes high if the author has more articles, co-authors and ties. Also, degree centrality has a significant positive relationship with the number of articles, betweenness, and ties. Findings related to correlations show that ϕ-index is a measure based on the number of articles and fixed teams of scientific collaboration while the centrality measures such as degree and betweenness are based on the number of articles, diversity in co-authors. This seems to be in contrast with the ϕ-index concept.
Article
Full-text available
This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, κ-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high κ-path centrality have high node betweenness centrality. The randomized algorithm runs in time O(κ3n 2−2αlog n) and outputs, for each vertex v, an estimate of its κ-path centrality up to additive error of ±n 1/2+α with probability 1 − 1/n 2. Experimental evaluations on real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared with existing randomized algorithms.
Article
Full-text available
Hierarchical decision models (HDMs) have been used widely in industrial and academic fields. The world of research in HDM is shrinking due to the connectivity of collaborative relationships between researchers. For new research to become widely known and for collaboration to be done in HDMs, we want to identify and connect the clusters of the researchers and the central member of the clusters. We use the social network analysis method to analyze the network of HDM researchers connected by coauthorship in the selected papers. We find out the important researchers with highest degree centralities and publication frequencies and the key researchers among the top 8 components. We act as the “information gatekeeper” by connecting the identified researchers so that the average distance between the vertices is eliminated significantly and thus construct a smaller world in the research realm about HDMs.
Book
Full-text available
This book constitutes the refereed proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph, WAW 2009, held in Barcelona, Spain, in February 2009 - co-located with WSDM 2009, the Second ACM International Conference on Web Search and Data Mining. The 14 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers address a wide variety of topics related to the study of the Web-graph such as theoretical and empirical analysis of the Web graph and Web 2.0 graphs, random walks on the Web and Web 2.0 graphs and their applications, and design and performance evaluation of the algorithms for social networks. The workshop papers have been naturally clustered in three topical sections on graph models for complex networks, pagerank and Web graph, and social networks and search.
Article
The abstract for this document is available on CSA Illumina.To view the Abstract, click the Abstract button above the document title.
Article
Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
Block Models and Allegiance, Thesis submitted to George Mason University in partial fulfillment of the M
  • J T Rigsby
Rigsby, J.T., 2005. Block Models and Allegiance, Thesis submitted to George Mason University in partial fulfillment of the M.S. in Statistical Science.