Community Structure In Social Networks Research Articles

Extremists of a feather flock together? Community structures, transitivity, and patterns of homophily in the US Islamist co-offending network.

Prior research suggests that members of terrorist groups prioritize forming network ties based on trust to improve their organizational and operational security. The homophily principle, which postulates that individuals tend to form relationships based on shared characteristics, can be a key mechanism through which people identify trustworthy associates. Next to homophily, the mechanism of establishing interconnected relationships through transitivity is also well-known to serve this purpose and shape community structures in social networks. We analyze the community structures of the Islamist co-offending network in the United States, which is highly violent, to assess whether homophily and transitivity determine which extremists form co-offending ties. We rely on a new database on the individual attributes and the co-offending relationships of 494 Islamist offenders radicalized in the United States between 1993 and 2020. Using community detection algorithms, we show that the US Islamist co-offending network is highly clustered, modular, and includes many small but only a few large communities. Furthermore, results from exponential random graph modeling show that transitive relationships as well as spatial proximity, ideological affiliation, and shared socio-cultural characteristics drive co-offending among US Islamist extremists. Overall, these findings demonstrate that the processes of homophily and transitivity shape violent social networks.

Product Promotion Strategies for Influencers in Social Network Associations

The development of the Internet has given birth to a new type of promotion mode, and the selection of promotion agents has become a hot issue. The influence index of the agent itself and the type of its association structure become the key factors affecting the degree of product promotion. In this study, we analyze different types of influencers and their community structure in social networks, study the influence of influencers' community structure and influence on information dissemination, and try to find out the agent selection strategy to obtain the highest promotion efficiency under the lowest budget. Based on these findings, this paper proposes an agent selection strategy for product promotion that integrates influencer type and association structure. The strategy suggests that firms first identify the social network structure of their target market and then select the combination of influencers that maximizes the effect of information dissemination. The findings of this study provide a new perspective and methodology for enterprises to promote their products in social networks, which helps them to utilize social network resources more effectively and enhance the effectiveness and efficiency of product promotion.

Identifying Community Structures in Social Networks using Discrete Harmony Search Algorithm

Identifying Community Structures in Social Networks using Discrete Harmony Search Algorithm

Uncovering the Local Hidden Community Structure in Social Networks

Hidden community is a useful concept proposed recently for social network analysis. Hidden communities indicate some weak communities whose most members also belong to other stronger dominant communities. Dominant communities could form a layer that partitions all the individuals of a network, and hidden communities could form other layer(s) underneath. These layers could be natural structures in the real-world networks like students grouped by major, minor, hometown, and so on. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We first expand the seed set from a single seed node based on our modified local spectral method and detect an initial dominant local community. Then we temporarily remove the members of this community as well as their connections to other nodes, and detect all the neighborhood communities in the remaining subgraph, including some “broken communities” that only contain a fraction of members in the original network. The local community and neighborhood communities form a dominant layer, and by reducing the edge weights inside these communities, we weaken this layer’s structure to reveal the hidden layers. Eventually, we repeat the whole process, and all communities containing the seed node can be detected and boosted iteratively. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy of detection which can be caused by global hidden community detection methods. Extensive experiments show that our method could significantly outperform the state-of-the-art baselines designed for either global hidden community detection or multiple local community detection.

Discovering community structure in social networks based on the synergy of label propagation and simulated annealing

Discovering community structure in social networks based on the synergy of label propagation and simulated annealing

Graph Transformer for Communities Detection in Social Networks

Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has low computational complexity for large-scale graphs. The contributions of the paper are summarized as follows. Firstly, a transformer is utilized to downsample the first-order proximities of the graph into a latent space, which can result in the structural properties and eventually assist in detecting the communities. Secondly, the fine-tuning task is conducted by tuning variant hyperparameters cautiously, which is applied to multiple social networks (Facebook and Twitch). Furthermore, the objective function (cross-entropy) is tuned by L0 regularization. Lastly, the reconstructed model forms communities that present the relationship between the groups. The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification. The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67 ± 0.04, 0.198 0.02, 0.228 ± 0.02, and 0.68 ± 0.03 on Wikipedia crocodiles, Github Developers, Twitch England, and Facebook Page-Page networks, respectively.

Crowd attraction-driven community evolution on social network

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

Where Online Friends Meet: Social Communities in Location-Based Networks

Recent research suggests that, as in offline scenarios, spatial proximity increases the likelihood that two individuals establish an online social connection, and geographic closeness could therefore influence the formation of online communities. In this work we present a study of communities in two online social networks with location-sharing features and analyze their social and spatial properties. We study the places users visit to understand whether communities revolve around places or whether they exist independently. Our results suggest that community structure in social networks may arise from both social and spatial factors, so that exploiting information about the places where people go could benefit the definition of new community detection methods and community evolution models.

Community Detection in Partially Observable Social Networks

The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks’ topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges . In this article, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac , a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an inferred Kronecker generative parameter matrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks.

An Ensemble of Community Detection in Social Networks Using Clustering of Users Demographic and Topological Information

Background: One of the great challenges in social network analysis is community detection. Community is a group of users which have high intra connections and sparse inter connections. Community detection or clustering reveals community structure of social networks and hidden relationships among their constituents. Nowadays, many different methods are proposed to detect community structures in social networks from different perspectives, but none of them can be a constant winner. Therefore, an ensemble of different methods can potentially improve the final result. Methods: In this paper, we present a framework for different methods to be combined for community detection. This method is a combination of genetic algorithms, particle swarm optimization, kmeans clustering and Louvain clustering algorithms. Our method uses topological and demographic information to identify communities and can automatically determine the number of optimal communities. Results and Conclusion: Quantitative evaluations based on extensive experiments on Ego- Facebook social network dataset reveals that the method presented in this study achieves favorable results, which are quite superior to other relevant algorithms in the literature. • Discovering relationships between individuals by analyzing social networks. • Providing identifying communities algorithms based on different clustering methods. • An ensemble of community detection consisting of GA, PSO, k-means and Louvain clustering. • The proposed method is better than the TSA method at silhouette and modularity criterion. Demographic information also relates to the profile of users and their shared tweets.

The Influence of Elite Networks on Green Policy Making

This article analyzes the relationship between the policy networks and the success of renewable energy and emission reduction policies. Arguing that strong connections between typologically different groups of elites are essential for good policies, the analysis looks at the Twitter community structure of the ministers in 34 countries. The results of the network study indicate that the success of renewable energy and CO2 emission reduction policy implementation is strongly related to the community structure in social networks of ministers. Looser network structures within policy communities and an abundance of relatively stronger connections between them are associated with higher renewable energy consumption and also lower CO2 levels, thus possibly with better policies. A case study on seven Nordic and Northern European countries complements and reinforces the theoretical expectations about deliberative policy making.Related ArticlesSabag‐Ben Porat, Chen, Azi Lev‐On, and Sam Lehman‐Wilzig. 2020. “Silent Partners: How Politicians' Facebook Communication with Constituents is Mediated.” Politics & Policy 48 (3): 550‐569. https://doi.org/10.1111/polp.12352Rowe, Andrew D., and David E. Pitfield. 2019. “The Challenge of Social Media Incorporation: A Case Study of HACAN Clearskies.” Politics & Policy 47 (4): 775‐806. https://doi.org/10.1111/polp.12319Tanaka, Yugo, Andrew Chapman, Tetsuo Tezuka, and Shigeki Sakurai. 2020. “Multiple Streams and Power Sector Policy Change: Evidence from the Feed‐In Tariff Policy Process in Japan.” Politics & Policy 48 (3): 464‐489. https://doi.org/10.1111/polp.12357[Correction added on December 17, 2020 after first online publication: The citation “Ben‐Porat, Chen Sabag, Azi Lev‐On, and Sam Lehman‐Wilzig. 2020” was edited to “Sabag‐Ben Porat, Chen, Azi Lev‐On, and Sam Lehman‐Wilzig. 2020“ to reflect correct spelling.]

Opposition-Based Genetic Algorithm for Community Detection in Social Networks

This paper proposes an improvised algorithm named modified crossover opposition-based genetic algorithm (MCOBGA) for community detection with the help of genetic algorithm (GA) to discover community structure in social networks. The paper deploys modified crossover and opposition-based initialization along with GA to improve the quality of the community structures. Initialization of the population through opposition-based learning ensures the improved selection of initial population, whereas modified crossover transmits information for improved community structure. The evaluations of proposed algorithm have been done on real-world networks. The experimental results show that MCOBGA has very competitive performance compared with GA with vertex similarity applied to community detection which has been the most similar approach to the proposed algorithm. Experimental results not only demonstrate improvement on convergence rate of the algorithm, but also communities discovered by proposed algorithm (MCOBGA) is highly inclined towards quality, compared to its counterpart. In this paper, we have focused on the community detection problem in the domain of the social network. Community detection is a very basic and hot research problem in complex networks. We have employed the genetic algorithm with modified crossover, opposition-based learning, and matrix encoding technique. We can use this technique in agriculture, the health sector, and market data analysis also.

Detecting community structure in signed and unsigned social networks by using weighted label propagation.

Detecting community structure is one of the most important problems in analyzing complex networks such as technological, informational, biological, and social networks and has great importance in understanding the operation and organization of these networks. One of the significant properties of social networks is the communication intensity between the users, which has not received much attention so far. Most of the proposed methods for detecting community structure in social networks have only considered communications between users. In this paper, using MinHash and label propagation, an algorithm called weighted label propagation algorithm (WLPA) has been proposed to detect community structure in signed and unsigned social networks. WLPA takes into account the intensity of communications in addition to the communications. In WLPA, first, the similarity of all adjacent nodes is estimated by using MinHash. Then, each edge is assigned a weight equal to the estimated similarity of its end nodes. The weights assigned to the edges somehow indicate the intensity of communication between users. Finally, the community structure of the network is determined through the weighted label propagation. Experiments on the benchmark networks indicate that WLPA is efficient and effective for detecting community structure in both signed and unsigned social networks.

Community-based rumor blocking maximization in social networks: Algorithms and analysis

Social networks provide us a convenient platform to communicate and share information or ideas with each other, but it also causes many negative effects at the same time, such as, the spread of misinformation or rumor in social networks may cause public panic and even serious economic or political crisis. In this paper, we propose a Community-based Rumor Blocking Problem (CRBMP), i.e., selecting a set of seed users from all communities as protectors with the constraint of budget b such that the expected number of users eventually not being influenced by rumor sources is maximized. We consider the community structure in social networks and solve our problem in two stages, in the first stage, we allocate budget b for all the communities, this sub-problem whose objective function is proved to be monotone and DR-submodular, so we can use the method of submodular function maximization on an integer lattice, which is different from most of the existing work with the submodular function over a set function. Then a greedy community budget allocation algorithm is devised to get an 1−1/e approximation ratio; we also propose a speed-up greedy algorithm which greatly reduces the time complexity for the community budget allocation and can get an 1−1/e−ϵ approximation guarantee meanwhile. Next we solve the Protector Seed Selection (PSS) problem in the second stage after we obtained the budget allocation vector for communities, we greedily choose protectors for each community with the budget constraints to achieve the maximization of the influence of protectors. The greedy algorithm for PSS problem can achieve a 1/2 approximation guarantee. We also consider a special case where the rumor just originates from one community and does not spread out of its own community before the protectors are selected, the proposed algorithm can reduce the computational cost than the general greedy algorithm since we remove the uninfected communities. Finally, we conduct extensive experiments on three real world data sets, the results demonstrate the effectiveness of the proposed algorithm and its superiority over other methods.

Revealing the strong and stable community structure in social networks

In a complex network, the global similarity standard is of high complexity and local similarity standard has biased error on node similarity. This paper puts forward a community discovering algorithm OBS, based on the degree of contribution of shared nodes in complex network. It distributes the contribution, using the degree of shared nodes as the unit, equally, which is made by shared nodes. Compared with average commute time standard, Random Walk with Restart standard and Hierarchical Structure Method standard, OBS standard has a more veracious forecast to the similar relationship between nodes. The experimental results of artificial network and typical complex network show that OBS algorithm has a better stability than GN, Fast-Newman (FN), and N-cut algorithms.

SSDBA: the stretch shrink distance based algorithm for link prediction in social networks

In the field of social network analysis, Link Prediction is one of the hottest topics which has been attracted attentions in academia and industry. So far, literatures for solving link prediction can be roughly divided into two categories: similarity-based and learning-based methods. The learning-based methods have higher accuracy, but their time complexities are too high for complex networks. However, the similarity-based methods have the advantage of low time consumption, so improving their accuracy becomes a key issue. In this paper, we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm (SSDBA). In SSDBA, we first detect communities of a social network and identify active nodes based on community average threshold (CAT) and node average threshold (NAT) in each community. Second, we propose the stretch shrink distance (SSD) model to iteratively calculate the changes of distances between active nodes and their local neighbors. Finally, we make predictions when these links’ distances tend to converge. Furthermore, extensive parameters learning have been carried out in experiments. We compare our SSDBA with other popular approaches. Experimental results validate the effectiveness and efficiency of proposed algorithm.

Markov Stability Analysis and Community Structure in Social Networks

Individuals connected to realistic networks exhibit collective behavior. In order to characterize this phenomenon and explore the correlation between collective behaviors and locally interacting elements, we use statistical methods and visualization software as a combined approach to understand the behavior of the network for a given behavior of the agents that we use to recreate our network. The aim of this work is to identify the communities as hierarchical structures trying to find them between a giant component and a small-world network. By analyzing the data and describing how these networks fall in community structure, we aim to obtain new tools and methodology which will help us to describe how networks grow and fall apart in smaller structures, which have similar features with the large network, but different dynamics.

Bounds on maximum concurrent flow in random bipartite graphs

Algorithms that exploit the duality between maximum concurrent flow and sparse cuts in a graph can effectively discover hierarchical community structures in social networks. We analyze the maximum concurrent flow on random bipartite graphs and compare the results to two bounds based on graph structure.. The minimum degree bound is based on a sparse cut that removes a node of minimum degree. The other is the “gridlock” bound, where all edges are saturated with flow. We show that the gridlock bound is more constraining when the minimum degree of the graph is sufficiently large, and provide a way to calculate this bound on graphs of diameter three, which occurs with high probability on larger random bipartite graphs.

Functional brain network architecture supporting the learning of social networks in humans

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.

Detecting the evolving community structure in dynamic social networks

Identifying the evolving community structure of social networks has recently drawn increasing attention. Evolutionary clustering, previously proposed to detect the evolution of clusters over time, presents a temporal smoothness framework to simultaneously maximize clustering accuracy and minimize the clustering drift between two successive time steps. Under this framework, evolving patterns of communities in dynamic networks were detected by finding the best trade-off between the clustering accuracy and temporal smoothness. However, two main drawbacks in previous methods limit the effectiveness of dynamic community detection. One is that the classic operators implemented by existing methods cannot avoid that a node is often inter-connected to most of its neighbors. The other is that those methods take it for granted that an inter-connection cannot exist between nodes clustered into the same community, which results in a limited search space. In this paper, we propose a novel multi-objective evolutionary clustering algorithm called DECS, to detect the evolving community structure in dynamic social networks. Specifically, we develop a migration operator cooperating with efficient operators to ensure that nodes and their most neighbors are grouped together, and use a genome matrix encoding the structure information of networks to expand the search space. DECS calculates the modularity based on the genome matrix as one of objectives to optimize. Experimental results on synthetic networks and real-world social networks demonstrate that DECS outperforms in both clustering accuracy and smoothness, contrasted with other state-of-the-art methods.