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The 113 th Congress of the United States. US Senate with 53 democrats, 45 republicans and 2 independent senators; and the US House of Representatives with 200 democrats, 233 republicans and 2 current vacant seats. (images from Wikimedia Commons).
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The media have always played an important role in society: it acts as catalyst of information for most people. With the advent of the 24-hour news channels, people became accustomed to having access to the information the media outlets provide anytime, anywhere. In this paper, we analyse the social network of politicians in the United States from t...
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... main parties control United States Congress: the Democratic and the Republican Parties. At the time of this study the current congress was referred to as the 113 th Congress of the United States which had a Democratic ma- jority in the Senate and a republican majority in the House of Representatives (as shown in Figure 1). 1 http://news.msn.com/politics/us-vote-shows-a-50-50-nation-give-or-take ...
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Citations
... Another network metric used with the INM model is called the Clustering Coefficient (CC) [MM13], which is important to be involved in the analysis of the INM model. It measures the tendency of an ICH to cluster with other ICHs in the INM model and can be formalized as follows: ...
This work presents a concise analysis of the world's intangible cultural heritage (ICH) practices. The study's analysis is performed in terms of the sustainable development goals (SDGs). To this end, a dataset from UNESCO for the period of 2008 to 2023 is used to generate three network models for the ICH and SDGs. Two network metrics are involved in the analysis, namely, closeness centrality and clustering coefficient. The generated networks are used to analyze the relations of the ICH and SDGs comprehensively. The findings show interesting facts about the international efforts to preserve the ICH of worldwide countries. It was also found that more information is needed to investigate the relationship between ICH and SDGs. Finally, the authors believe that there exists a bias in adopting SDGs for different continents, which could be due to the lack of information.
... The network model shown in Figure is displayed in a 2D dimension. This approach is frequently used in visualizing a wide range of applications such as biological networks [5], social networks [6], political networks [7], and transportation networks [8]. The problem with such a visualization technique is that when having giant network models in terms of the number of nodes and edges, the 2D visualization makes it difficult to understand the complex and dense connections among nodes. ...
The current revolution in technology forces most daily life systems to generate large-scale data. Understanding such data is not an easy task due to its size and complexity. Therefore, data visualization has played a key role in understanding data more clearly. Moreover, most of the available approaches for data visualization are considered traditional that deal with data in a 2D way. Furthermore, one of the most modern methods for data visualization is converting data into network models that include nodes and edges. This kind of visualization has been proven as an efficient method of analyzing large-scale data. On the other hand, with the advent of virtual reality technology and the metaverse concept, data can be visualized and analyzed in an interactive and 3D way. However, there is a lack in the literature providing enough information on how to generate such 3D models. This thesis comes to investigate the integration of virtual reality technology and complex networks, and how this integration can generate interactive data visualization models. To this end, a 3D VR-based interactive biological network model is designed and implemented to be adoptable by the metaverse platform. The biological network represents the gene-gene interactions of the sex chromosomes. The developed model enables analysts to dive into data and interact with its objects, which lead to a more professional, deep, and accurate analysis. The model proved its efficiency in terms of usability when tested by experts.
... The value of 3.275 is considered acceptable compared to the ACM network, which means there are authors who play the role of a bridge between the communities of authors, and this is clear when observing the Average Clustering Coefficient. This metric is proven to be efficient in measuring the performance of a network, as shown in (Mahmood and Menezes, 2013). Furthermore, other network measurements play a significant role in evaluating a network. ...
... The second face of the proposed model is to use network measurement in two levels; network and node levels as follows [30][31]: ...
The field of Artificial Intelligence (AI) has noticeably developed in recent years. The literature includes a tremendous number of contributions proposed by worldwide researchers. These algorithms can be used for a variety of purposes such as classifications, clustering, and forecasting, to mention a few. The problem that researchers frequently face is the appropriateness of the algorithm they use for their data. Moreover, researchers usually benchmark their work with the literature, which also needs to select appropriate algorithms that fit their needs. Another problem that researchers face is that when developing a new algorithm or modify an existing they need to have extensive knowledge about algorithms. These issues may take a lot of time and effort that are consumed in exploring the literature. This research comes to provide a network model as a guide that gathers most of the AI available algorithms in the literature aiming to make it easier for researchers to understand AI algorithms. The proposed network model includes nodes and edges. Each node represents an AI algorithm and the edges are created among them when they have parameters in common. The findings show the efficiency of the proposed model in supporting researchers in selecting appropriate algorithms for their works. Finally, this work is considered novel since it collects most of the AI algorithms in a standalone model.
... The generated graph is called a Complex Network. There are many kinds of complex networks such as political networks [4], collaboration networks [5] [6], biological networks [7], social networks [8], crime networks [9], etc. Figure When Mentioning the Coalitions. ...
... This number of parties is considered to be big taking into considerations the population size and the area of Iraq. In the big countries the number of parties is significantly less such that in Brazil there are 30 parties [4] and in the United States there is only two parties [5]. The way of forming parties in Iraq is different than what can be seen in most of the worldwide countries [6]. ...
... To this end, we created nodes and edges, where a node represents a party and the edges are created when there exists coalition among them. In fact, this approach of forming data is followed in many works in the literature such as [5]. ...
Data science has become a dominant tool in many different disciplines. Many methods and approaches are available and can be used in analyzing data. Network science approach is currently considered a powerful tool that is able to visualize and analyze complex data through investigating the relations among data objects. This kind of methods considers data as nodes that are connected by edges among them, and these connections are created based on a particular strategy. In this work, we generate a network model for the Iraqi parties using the IHEC public dataset. The model consists of nodes and edges connecting them. The performance of the generated network model was evaluated using two-level of measurements; Network-Level measurements (i.e., density, average degree, degree distribution, average path length, and average clustering coefficient) and Node-Level measurements (i.e., betweenness centrality and degree centrality). The visualization and the analysis of the network showed interesting facts about the collaboration patterns among the Iraqi parties. This work also showed that network science is useful in analyzing complex and highly related data. It can also reveal some hidden patterns that might be existed within the data.
... This number of parties is considered to be big taking into considerations the population size and the area of Iraq. In the big countries the number of parties is significantly less such that in Brazil there are 30 parties [4] and in the United States there is only two parties [5]. The way of forming parties in Iraq is different than what can be seen in most of the worldwide countries [6]. ...
... To this end, we created nodes and edges, where a node represents a party and the edges are created when there exists coalition among them. In fact, this approach of forming data is followed in many works in the literature such as [5]. ...
Data science has become a dominant tool in many different disciplines. Many methods and approaches are available and can be used in analyzing data. Network science approach is currently considered a powerful tool that is able to visualize and analyze complex data through investigating the relations among data objects. This kind of methods considers data as nodes that are connected by edges among them, and these connections are created based on a particular strategy. In this work, we generate a network model for the Iraqi parties using the IHEC public dataset. The model consists of nodes and edges connecting them. The performance of the generated network model was evaluated using two-level of measurements; Network-Level measurements (i.e., density, average degree, degree distribution, average path length, and average clustering coefficient) and Node-Level measurements (i.e., betweenness centrality and degree centrality). The visualization and the analysis of the network showed interesting facts about the collaboration patterns among the Iraqi parties. This work also showed that network science is useful in analyzing complex and highly related data. It can also reveal some hidden patterns that might be existed within the data.
... Moreover, the average clustering coefficient [11] is also weak and reflects a low tendency of the roads to cluster together. The other feature is the average path length [12] [13], which is acceptable since it means that less than 3 roads are needed to access any region in the city. In fact, the general characteristics of the network are not sufficient in terms of the aforementioned measurements compared to similar networks in the literature. ...
In recent years, the city of Mosul, which is the capital city of Nineveh province in the North of Iraq, had witnessed an unstable situation (e.g., wars, internal conflicts) that led to destructing most of the infrastructure including city roads. In addition, the population of Mosul is currently concentrated on the east coast of the city. Therefore, this situation has caused a server traffic jam and the roads have become overloaded, which is time-wasting when accessing a particular place in the city. In this analytical study, the roads of the east coast of Mosul city are modeled in the form of a Road Network. The proposed approach is based on concepts inspired from Complex Networks and their measurements such as clustering coefficient, betweenness, degree, and closeness. The dataset of this work was collected from Google Earth with the support of governmental offices and road-experienced individuals. The created network represents the road network of Mosul city. In the results, suggestions and recommendations are provided, which can contribute to alleviating the problem of traffic congestion in the city of Mosul. The provided suggestions do not need a high cost because the proposed approach benefits the current road networks with few modifications. The proposed approach is applicable to any city of interest.
... In IFN, it reflected the importance of an individual in connecting groups or individuals. The Cb of individual j can be defined as follows [33]: ...
... • Closeness Centrality Cc: Represents the reciprocal of the sum of all the shortest paths of a node to other network nodes. In IFN, it determined how close an individual to other individuals and can be described by [33]: ...
Online social networks have become one of the most effective ways for connecting and communicating with people. These networks play a significant role in our business, social, and daily activities. In these networks, people follow a particular behavior that is not necessarily identical to the actual behavior in their real life. Our goal in this work is to investigate and explore the interactions among people in the Facebook network as our targeted social network. Our investigation aims to detect the potential anomalous behaviors within the interactions among people. To this end, we involve the structural and spectral features of the network in proposing a new approach for anomaly detection. Besides, our approach is supported by concepts that are inspired from sociological theories. The data of this article was extracted from the Facebook network using Facebook Graph API. In the experimental results, the proposed approach reflected an efficient performance in terms of detecting potential anomalies and computational complexity compared to other approaches in the literature.
... It shows the more probable information receiving nodes in the network. The C b of a node j can be defined as follows [39], [40]: ...
In the current technological era, scientific research is considered as one of the crucial factors for developing human life. The main sources for producing scientific research are worldwide universities, institutions, research centers, and scientific laboratories. Therefore, it is important to evaluate the performance of these institutions in terms of research production and quality. The main reason for this evaluation is to improve the performance of researchers and eventually reflect this improvement in scientific research status. Moreover, the productivity and quality of the researchers in a particular university can be measured based on two main indicators, namely, research citations and research publishing venues.
In this thesis, the current scientific status of the main Iraqi universities is deeply investigated. To this end, a Citation Network is generated among them. This kind of network can reflect the actual scientific research status of the main Iraqi universities. The approach that is used in this thesis is based on the concepts of complex networks. For the data collection, a special-purpose program is designed to crawling the Google scholar repository and retrieve all the required data. This crawler is designed to collect the published research articles based on the official educational domains of the Iraqi universities.
The first main contribution of this work is to generate a citation network of the Iraqi main universities and extract the main facts on scientific research activities. The second contribution is proposing a local rank for the main Iraqi universities based on network measurements and other academic indicators. Another aspect that is investigated in this work is the scientific collaboration among the Iraqi universities and with the worldwide universities. Furthermore, this thesis also shows the current status of the Iraqi universities compared to the world in terms of the Scopus repository. Based on the obtained results, this thesis provides recommendations and suggestions on how to improve the performance of Iraqi universities in terms of scientific research and scientific collaboration among the universities.
The obtained results show an on-average performance of the scientific research in Iraqi universities according to network measurements such as the average clustering coefficient and the average path length. However, the University of Baghdad outperformed the other Iraqi universities in terms of the frequency of citations and the other network measurements. Also, the-top cited author was from the University of Baghdad in the field of Medicine with about 15566 citations (to the date of writing this thesis). However, the performance of scientific research in Iraq underperforms the neighbored countries such as Turkey, Iran, and KSA in terms of h-index, the number of the published papers, total citations, and the average citation per paper. The results also show that the collaboration among the Iraqi universities is based on the geographical area.
