Figure 3 - uploaded by Nan du
Content may be subject to copyright.
Source publication
- [...]
Recent years have seen that WWW is becoming a flourish-ing social media which enables individuals to easily share opinions, experiences and expertise at the push of a sin-gle button. With the pervasive usage of instant messaging systems and the fundamental shift in the ease of publish-ing content, social network researchers and graph theory re-sear...
Context in source publication
Context 1
... a result, the club splits into two smaller commu- nities with the administrator and the teacher being as the central persons accordingly. Figure 3 shows the detected two communities by ComTector which are exactly matched with the result of Zachary's study. Games are more frequent between members of the same conference than between members of different confer- ences. ...
Similar publications
Real world networks often have community structure. It is characteristic that the groups of nodes are connected denser within themselves and rarely with each other. The Girvan-Newman method for the detection and analysis of community structure is based on the iterative elimination of edges with the highest number of the shortest paths that go throu...
Citations
... Networks provide a powerful framework for modeling the structure and dynamics of complex systems by representing them as nodes connected by edges [1,2]. This framework finds broad application across diverse fields, including social networks [3][4][5], transportation systems [6][7][8], finance [9][10][11], and neuroscience [12][13][14]. The modern science of networks seeks to unravel both the structural and functional aspects of these systems, where nodes represent fundamental units and edges denote their interactions [15]. ...
Community detection, also known as graph partitioning, is a well-known NP-hard combinatorial optimization problem with applications in diverse fields such as complex network theory, transportation, and smart power grids. The problem's solution space grows drastically with the number of vertices and subgroups, making efficient algorithms crucial. In recent years, quantum computing has emerged as a promising approach to tackling NP-hard problems. This study explores the use of a quantum-inspired algorithm, Simulated Bifurcation (SB), for community detection. Modularity is employed as both the objective function and a metric to evaluate the solutions. The community detection problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling seamless integration with the SB algorithm. Experimental results demonstrate that SB effectively identifies community structures in benchmark networks such as Zachary's Karate Club and the IEEE 33-bus system. Remarkably, SB achieved the highest modularity, matching the performance of Fujitsu's Digital Annealer, while surpassing results obtained from two quantum machines, D-Wave and IBM. These findings highlight the potential of Simulated Bifurcation as a powerful tool for solving community detection problems.
... The above node-node similarity can be used to detect the communities. The main idea of many structured-based community detection algorithms is grouping nodes based on the fact that when the similarity of two nodes is significant, they have more chance to be in a community 35,36 . Since overlapping nodes are highly influential nodes 37,38 , this formula also works for overlapping communities. ...
Numerous algorithms have been proposed to infer the underlying structure of the social networks via observed information propagation. The previously proposed algorithms concentrate on inferring accurate links and neglect preserving the essential topological properties of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. DANI is constructed using the Markov transition matrix, which is derived from the analysis of time series cascades and the observation of node-node similarity in cascade behavior from a structural perspective. The presented method has linear time complexity. This means that it increases with the number of nodes, cascades, and the square of the average length of cascades. Moreover, its distributed version in the MapReduce framework is scalable. We applied the proposed approach to both real and synthetic networks. The experimental results indicated DANI exhibits higher accuracy and lower run time compared to well-known network inference methods. Furthermore, DANI preserves essential structural properties such as modular structure, degree distribution, connected components, density, and clustering coefficients. Our source code is available on GitHub (https://github.com/AryanAhadinia/DANI).
... In the field of sociology, researchers have found that communities generally exist in various complex networks [13]. In recent years, with the rise of social networks, the attention in the field of social network analysis has greatly increased [14,15], including research on community detection algorithms. Currently, community detection algorithms can be mainly divided into topology-based community detection algorithms, attribute-based community detection algorithms, and hybrid algorithms that integrate topology and attributes. ...
With the increasing diversity and complexity of online social networks, effectively dividing communities presents a growing challenge. These networks are characterized by their large scale, sparse structure, and numerous isolated points. Traditional community detection methods lack consideration of node attribute information, thereby negatively impacting the accuracy of community detection. To address these challenges, this paper presents a novel Louvain-FTAS community detection algorithm that integrates topology and attribute structure. The proposed algorithm first selects attributes with positive effects to account for attribute heterogeneity. Subsequently, it utilizes a semi-local strategy to calculate topology similarity and information entropy to calculate attribute similarity. These values are combined to obtain the final node similarity matrix, which is then fed into the Louvain algorithm to maximize modularity and incorporate multi-dimensional attribute features to enhance community detection accuracy. The proposed model is evaluated through comparative experiments on two real datasets and artificial synthetic networks, demonstrating its rationality and effectiveness.
... Within this setup, games occurred more frequently among teams within the same conference compared to those from different conferences. This pattern suggests that each conference functions as an individual community within the overall network [58]. The network was proposed by Girvan and Newman. ...
With the growth of online networks, understanding the intricate structure of communities has become vital. Traditional community detection algorithms, while effective to an extent, often fall short in complex systems. This study introduced a meta-heuristic approach for community detection that leveraged a memetic algorithm, combining genetic algorithms (GA) with the stochastic hill climbing (SHC) algorithm as a local optimization method to enhance modularity scores, which was a measure of the strength of community structure within a network. We conducted comprehensive experiments on five social network datasets (Zachary's Karate Club, Dolphin Social Network, Books About U.S. Politics, American College Football, and the Jazz Club Dataset). Also, we executed an ablation study based on modularity and convergence speed to determine the efficiency of local search. Our method outperformed other GA-based community detection methods, delivering higher maximum and average modularity scores, indicative of a superior detection of community structures. The effectiveness of local search was notable in its ability to accelerate convergence toward the global optimum. Our results not only demonstrated the algorithm's robustness across different network complexities but also underscored the significance of local search in achieving consistent and reliable modularity scores in community detection.
... For example, previous research has found that characteristics of both friends and friends of friends independently predict students' college aspirations and their risk of dropping out of high school [67]. In a social network analysis, a community is defined as some sort of cohesive substructure with a high degree of connectivity [68]. Some members of the community may have an important impact on the community. ...
This study aimed to detect college students’ daily peer networks through a behavioral big-data-driven social network analysis and to explore the relationship between college students’ daily peer relationships and academic achievement. We collected data on the class attendance, eating, and bathing records of 4738 undergraduate students who entered a university in 2018 to infer the daily peer relationship networks of students. The Louvain algorithm and some network indicators such as density and average clustering coefficient were used to investigate social network characteristics of peer relationship networks. The findings show that initially, students in the same dormitory tended to form daily peer relationships, gradually shifting toward relationships centered on classmates as time progressed. These peer networks often aligned with geographical location and living areas. Moreover, the peers of college students who received high-level scholarships were more likely to receive scholarships. The number of peers was positively correlated with the likelihood of receiving a scholarship. The research findings contribute to the application of information technology to promote the sustainable development of higher education and individual students.
... Ying and Zhou (2010) investigated the lexical errors in the Chinese translation of the English drug package inserts based on Carl James' error taxonomy to highlight common causes and propose practical tactics to reduce such errors. Du et al. (2007) reported the syntactic structures used in English titles of medical research articles. Finally, Zhang and Cheung (2022) investigated the frequencies and distribution of modal verbs in Chinese-English governmental press conference interpreting. ...
... . These communities in networks signify groups of nodes that display stronger interconnections amongst themselves compared to connections with nodes outside the community [8]. These communities provide insightful information on the structure, organization, and dynamics of complex systems [9,10]. The applications of community detection across various domains, such as social network analysis, biological networks, online forums, and recommendation systems, have garnered substantial attention [11]. ...
Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.
... Links represent the interactions between the nodes, which serve as the system's fundamental building elements in this instance. [Du N, et al. (2007)]. Since identifying community structure is a difficult issue, several strategies, including modularity optimization, dynamic label propagation, statistical inference, spectral clustering, information-theoretic methods, and topologybased methods, have been developed in the past ten years. ...
The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings.
... Network structure has been widely applied to represent the relationship among a vast number of entities, which finds applications in various domains, ranging from social networks (Du et al. 2007;Leskovec, Lang, and Mahoney 2010), biological networks (Rahiminejad, Maurya, and Subramaniam 2019;Calderer and Kuijjer 2021), to scientific networks (Jung and Segev 2014;Gao et al. 2021). It is also interesting to note that nodes commonly interact with each other from different aspects, leading to multi-layer networks. ...
... In addition, denote A :,i 2 ,i 3 ∈ R I 1 , A i 1 ,:,i 3 ∈ R I 2 , and A i 1 ,i 2 ,: ∈ R I 3 as the (i 2 , i 3 )th mode-1, (i 1 , i 3 )th mode-2 and (i 1 , i 2 )th mode-3 fiber of A, respectively. For j ∈ [3], let M j (A) be the modej matricization of A (Kolda and Bader 2009), which unfolds A by concatenating its mode-j fibers of A horizontally. Specifically, ...
... Proof of Lemma 5: First, it follows from Lemma 2 that the columns of UO (1) are the left singular vectors corresponding to the nonzero singular values of M 1 ( P)(V ⊗ U). Following the similar argument in Lemma 2, there exists an orthogonal matrix O (3) such that UO (3) are the singular vectors corresponding to the first K leading singular values of M 1 ( A)(V ⊗ U). By Theorem 4 in Yu, Wang, and Samworth (2015), there exists an orthogonal matrix O (4) such that ...
Communities in multi-layer networks consist of nodes with similar connectivity patterns across all layers. This article proposes a tensor-based community detection method in multi-layer networks, which leverages available node-wise covariates to improve community detection accuracy. This is motivated by the network homophily principle, which suggests that nodes with similar covariates tend to reside in the same community. To take advantage of the node-wise covariates, the proposed method augments the multi-layer network with an additional layer constructed from the node similarity matrix with proper scaling, and conducts a Tucker decomposition of the augmented multi-layer network, yielding the spectral embedding vector of each node for community detection. Asymptotic consistencies of the proposed method in terms of community detection are established, which are also supported by numerical experiments on various synthetic networks and two real-life multi-layer networks.
... Community detection algorithms have been extended to MLNs for identifying tightly knit groups of nodes based on different feature combinations ( [22,13].) Algorithms based on matrix factorization [10], cluster expansion [12], Bayesian probabilistic models [23], regression [9] and spectral optimization of the modularity function based on the supra-adjacency representation [25] have been developed. ...
Any data analysis, especially the data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is a lot of attention paid to the first two of the above components as part of research whereas the understanding as well as deriving actionable decisions is quite tricky. Visualization is an important step towards both understanding (even by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or prone to a spike in cases in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on an extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user-interaction, visualization management, and complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speedup development by different groups, and iii) address efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the implementation of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back end modules.
