Community Structure In Social Networks Research Articles

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.

Research on the discovery of opinion leaders in social networks

Since entering the era of Wed 2.0, social networks have been an indispensable part of human life. At present, these social networks are the main source of information dissemination and alienation, and they play a key role in the process of dissemination and are the central users of social networks. Central users have an incredible ability to communicate in their community, and their knowledge and experience of things have a great impact on other users in the community. This research mainly uses the analysis method of social network to analyze the community and find the central users in the community. We use Louvain algorithm to identify the community structure in social networks, and on this basis we use PageRank algorithm to analyze the central users in each community.

Community Detection in Social Networks: Literature Review

Due to easy and cost-effective ways, communication has amplified many folds among humans across the globe irrespective of time and geographic location. This has led to the construction of an enormous and a wide variety of social networks that is a network of social interactions or personal relations. Social network analysis (SNA) is the inspection of social networks in order to understand the participant’s arrangement and behaviour. Discovering communities from the social network has become one of the key research areas in SNA. Communities discovered from social networks facilitate its members so as to interact with relatable people who have similar or comparable interests. However, in present time, the enormous growth of social networks demands an intensive investigation of recent work carried out for identifying community division in social networks. This paper is an attempt to enlighten the ongoing developments in the domain of Community detection (CD) for SNA. Additionally, it sheds light on the algorithms which use meta-heuristic optimisation techniques to hit upon the community structure in social networks. Further, this paper gives a comparison of proposed methods in recent years and most frequently used optimisation approaches in the domain of CD. It also describes some application areas where CD methods have been used. This guides and encourages researchers to probe and take ahead the work in the area of detecting communities from social networks.

The Influence of Community Structure on the Diffusion of Knowledge—A View Based on Market Segmentation

To analyze the effects of different seeding selection strategies on knowledge diffusion in education field, this study builds an ABMS spreading model and performs several simulation experiments. Besides, market segmentation is proposed as the methods of community recognition as, the effects of the mechanism of market segmentation on seeding user choosing strategies are in-vestigated, and knowledge diffusion efficiency is analyzed. Given the existing education community structure in social network of knowledge, and the forma-tion mechanism of network, and even if the multiple seeds locate in the same education community, they cannot effectively exert the knowledge diffusion function of each seeding node. Several studies have showed that random selec-tion strategy is more effective than the sensitive strategy without any market segmentation. The seeding strategy integrated with market segmentation is ca-pable of improving the efficiency of knowledge diffusion significantly. In the meantime, the sensitive seeding strategy under the education community recog-nition can achieve better knowledge diffusion efficiency.

An approach based on mixed hierarchical clustering and optimization for graph analysis in social media network: toward globally hierarchical community structure

As the massive size of contemporary social networks poses a serious challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities, we develop, in this manuscript, an approach used to discover hierarchical community structure in large networks. The introduced hybrid technique combines the strengths of bottom-up hierarchical clustering method with that of top-down hierarchical clustering. In fact, the first approach is efficient in identifying small clusters, while the second one is good at determining large ones. Our mixed hierarchical clustering technique, based on the assumption that there exists an initial solution composed of k classes and the combination of the two previously mentioned methods, does not the change of the number of partitions, modifies the repartition of the initial classes. At the end of the introduced clustering process, a fixed point, representing a local optimum of the cost function which measures the degree of importance between two partitions, is obtained. Consequently, the introduced combined model leads to the emergence of local community structure. To avoid this local optimum and detect community structure converged to the global optimum of the cost function, the detection of community structures, in this study, is not considered only as a clustering problem, but as an optimization issue. Besides, a novel mixed hierarchical clustering algorithm based on swarms intelligence is suggested for identifying community structures in social networks. In order to validate the proposed method, performances of the introduced approach are evaluated using both real and artificial networks as well as internal and external clustering evaluation criteria.

Individual differences in learning social and nonsocial network structures.

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between nonsocial bits of information? Here, the authors use a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. They examined individual differences in the ability to learn community structure of networks composed of social versus nonsocial stimuli. Although participants were able to learn community structure of both social and nonsocial networks, their performance in social network learning was uncorrelated with their performance in nonsocial network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of nonsocial community structure. Taken together, the results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in nonsocial networks. The study design provides a promising approach to identify neurophysiological drivers of social network versus nonsocial network learning, extending knowledge about the impact of individual differences on these learning processes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A new algorithm for communities detection in social networks with node attributes

Revealing the community structure in social networks witnessed a determined effort. In this respect, a different category of social network can be handled, such as, dynamic social networks, social networks with node attributes, etc. In this article, we introduce a new method to solve this thriving issue in the social network with node attributes. This latter can be represented by a bipartite graph, which consists of a two sets of nodes and edges connecting these nodes. The tendency of people with similar node attributes leads to the hidden information of clusters or communities. A wealthy number of community-detection algorithms have been proposed for bipartite graphs and applied to several domains in the literature. To palliate some of the highlighted shortcomings, we introduce a new approach, called Fast-Bi Community Detection (FBCD), that aims to an efficient community detection in social networks. The main idea of this approach is to explore the set of maximum matching in the bipartite graph in order to reduce the complexity of our algorithm. The carried out experiments show the high quality of the obtained communities versus those by the pioneering ones of the literature.

Fast detection of community structures using graph traversal in social networks

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real-time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in O(|V | + |E|) time to create an initial cover before using modularity maximization to get the final cover. Keywords - community detection; Influenced Neighbor Score; brokers; community nodes; communities

Local Core Members Aided Community Structure Detection

The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to core nodes, then makes judgement if the node should be classified in the community or not, and finally form the process of community detection. The experimental results show that the proposed method can mine the social structure efficiently with a low computational complexity.

Community outlier detection in social networks based on graph matching

Outlier or anomaly detection is widely used in several fields of study such as statistics, data mining, and social networks. It can reveal important anomalous and interesting outlier behaviours in the social network communities. In this work, we propose a new approach for community outlier detection based on social network graph matching. We identify community structures in social networks using some community detection methods. For each community, the node signature is combined with an optimal assignment method for matching the original graph data with the graph pattern data, in order to detect two formalised anomalies: anomalous nodes and anomalous edges. We also define a distance between two graphs using Euclidean formula. Then, we define a node-to-node cost in an assignment problem using the Hungarian method to deduce the matching function. The obtained experimental results demonstrate that our approach performs on real social network datasets.

Community outlier detection in social networks based on graph matching

Outlier or anomaly detection is widely used in several fields of study such as statistics, data mining, and social networks. It can reveal important anomalous and interesting outlier behaviours in the social network communities. In this work, we propose a new approach for community outlier detection based on social network graph matching. We identify community structures in social networks using some community detection methods. For each community, the node signature is combined with an optimal assignment method for matching the original graph data with the graph pattern data, in order to detect two formalised anomalies: anomalous nodes and anomalous edges. We also define a distance between two graphs using Euclidean formula. Then, we define a node-to-node cost in an assignment problem using the Hungarian method to deduce the matching function. The obtained experimental results demonstrate that our approach performs on real social network datasets.

Identifying Influential Individuals on Large-Scale Social Networks: A Community Based Approach

Identifying a small subset of influential individuals on social networks can bring great benefits for many practical applications like viral marketing. This issue is typically formulated as the influence maximization problem. As a fundamental research topic in social network analysis, influence maximization has attracted much attention in recent years. In general, traditional influence maximization algorithms can be classified into two categories: 1) greedy algorithms, which possess high-performance guarantee but are time-consuming and 2) heuristic algorithms, which are time-efficient but lack performance guarantee. In this paper, we first propose a community detection approach based on network embedding to detect the community structures of social networks. With the aid of these community structures, we then propose two novel and robust community-based approximation algorithms, basic community-based robust influence maximization (BCRIM) and improved community-based robust influence maximization (ICRIM), to combat the problem of influence maximization. Both BCRIM and ICRIM have high-performance guarantee as well as high efficiency, while ICRIM runs even faster than BCRIM. Specifically, BCRIM and ICRIM identify influential individuals within communities rather than the entire network. The influence scope of each individual in BCRIM and ICRIM is restricted to its community and its neighbors’ communities; thus, they are able to simultaneously identify influential individuals within communities and important hub or bridge individuals that connect different communities together. Furthermore, we analyze the performance guarantee of BCRIM and ICRIM in detail. Finally, we conduct extensive experiments on five benchmark networks to evaluate the performance of the proposed algorithms.

Overlapping community detection based on discrete biogeography optimization

Community detection can be used to help mine the potential information in social networks, and uncovering community structures in social networks can be regarded as clustering optimization problems. In this paper, an overlapping community detection algorithm based on biogeography optimization is proposed. Firstly, the algorithm takes the method of label propagation based on local max degree and neighborhood overlap for initial network partitioning. The preliminary partition result used to construct initial population by cloning and mutating to accelerate the algorithm’s convergence. Next, to make biogeography optimization algorithm suitable for community detection, we design problem-specific migration rules and mutation operators based on a novel affinity degree to improve the effectiveness of the algorithm. Experiments on benchmark test data, including two synthetic networks and four real-world networks, show that the proposed algorithm can achieve results with better accuracy and stability than the compared evolutionary algorithms.

A cooperative game framework for detecting overlapping communities in social networks

Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms.

Research on Multi-label Propagation Algorithm for Community Detection Based on Weibo

Community structure in social network has an overlapping property from where people can find a lot of valuable information by community detection. It will help users to make accurate and valuable judgments as well as decisions. However, the increasing amount of information has led to difficulties for traditional technology to meet the requirement of big data processing. In order to effectively process data in large-scale network, a multi-label propagation algorithm for community detection has been implemented by using BSP parallel model. This algorithm combines the idea of LPA and COPRA. It finds out communities with active Weibo users, and then uses these communities as labels to perform label propagation. In the end, it classifies the remaining users’ communities. The algorithm solves LPA’s problems of instability and low accuracy. Its effectiveness and efficiency were tested and verified by using data from Weibo network.

A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks

A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks

Network Redundancy and Information Diffusion: The Impacts of Information Redundancy, Similarity, and Tie Strength

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.

Secure Social Multimedia Big Data Sharing Using Scalable JFE in the TSHWT Domain

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the meantime, social networking platforms based on cloud computing have made multimedia big data sharing in social networks easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use, such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social networks. In this article, a joint fingerprinting and encryption (JFE) scheme based on tree-structured Haar wavelet transform (TSHWT) is proposed with the purpose of protecting media distribution in social network environments. The motivation is to map hierarchical community structure of social networks into a tree structure of Haar wavelet transform for fingerprinting and encryption. First, fingerprint code is produced using social network analysis (SNA). Second, the content is decomposed based on the structure of fingerprint code by the TSHWT. Then, the content is fingerprinted and encrypted in the TSHWT domain. Finally, the encrypted contents are delivered to users via hybrid multicast-unicast. The proposed method, to the best of our knowledge, is the first scalable JFE method for fingerprinting and encryption in the TSHWT domain using SNA. The use of fingerprinting along with encryption using SNA not only provides a double layer of protection for social multimedia sharing in social network environment but also avoids big data superposition effect. Theory analysis and experimental results show the effectiveness of the proposed JFE scheme.

INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

With the proliferation of graph applications in social network analysis, biological networks, WWW and many other areas, a great demand of efficient and scalable algorithms for graph mining is rising. In many applications, finding the most influential nodes in the network is informative for the network analyzers in order to track the spread of information, disease and rumors. The problem of finding the top k influential nodes of a directed graph G=(V,E) such that the influence spread of these nodes will be maximized has long been exposed and many algorithms have been proposed to deal with this problem. Despite the useful characteristics of community structure in social networks, only a few works have studied the role of communities in the spread of influence in social networks.In this paper we propose an efficient algorithm (which has an acceptable response time even for large graphs) for finding the influential nodes in the graph under linear threshold model. We exploit the community structure of graph to find the influential communities, and then find the influence of each node as a combination of its local and global influences. We compare our algorithm with the state-of-the-art methods for influence maximization problem and the results of our experiments on real world datasets show that our approach outperforms the other ones in the quality of outputted influential nodes while still has acceptable running time and memory usage for large graphs.

New Methods for Generating Synthetic Equivalents of Real Social Networks

In both empirical and theoretical contexts, we may need to generate synthetic versions of large-scale social networks. Extant network generation models and algorithms do not replicate in full measure the structural complexities of real social networks. While extant approaches can replicate specific network characteristics well (e.g., degree distribution), they fail to replicate other aspects such as presence of overlapping cliques and community structures in social networks. Inspired by some recent developments in community detection algorithms in social networks, we propose two new network generation algorithms that explicitly incorporate the distribution of overlapping communities. We show that our proposed algorithms are measurably superior to the extant algorithms in generating synthetic social networks that more closely resemble actual social networks along a multiplicity of metrics that are used to characterize such networks.