Community Detection In Social Networks Research Articles

Geographical Structural Features of the WeChat Social Networks

Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.

Social network community analysis based large-scale group decision making approach with incomplete fuzzy preference relations

Large-scale group decision making (LSGDM) is characterized by a large number of participators, multiple groups and a mass of decision data provided by the participators. With the development of social media and e-democracy technologies, the social relationships among group decision makers should be taken into consideration when we address LSGDM problems with incomplete fuzzy preference relations. In this paper, a social network community detection approach of social networks based on the fuzzy clustering method is proposed. Then, a method of repairing incomplete fuzzy preference relations based on the divided communities is proposed. Moreover, we have proposed a two-stage consensus reaching method to balance the number of iterations and modification range of original decision information, in which the group closeness centrality of community is applied to measure its importance. Also, the procedure for LSGDM with fuzzy preference relations based on social network community analysis is established. The feasibility and advantages of the proposed method for LSGDM based on social network community analysis are illustrated by an illustrative example of a flexible manufacturing system.

Modularity based Community Detection in Social Networks

The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

Intelligent Community Detection: Review

An emerging multidisciplinary field nowadays is graph mining; which is basically a form of data mining that deals with graphs instead of normal data, (Cook & Holder, 2006) it intends to discover repetitive sub-graphs and interesting patterns that occurs in the input graph. This research is focusing on community detection using graph mining techniques; the objectives are to study the existing methods used to solve this problem by comparing the evaluation parameters, and then develop a method for community detection in social networks, which meets the criteria identified for assessing a good algorithm. Various techniques of graph mining will be tested to determine wither it can meet the identified criteria.

Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users’ attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.

AntLP: ant‐based label propagation algorithm for community detection in social networks

In social network analysis, community detection is one of the significant tasks to study the structure and characteristics of the networks. In recent years, several intelligent and meta-heuristic algorithms have been presented for community detection in complex social networks, among them label propagation algorithm (LPA) is one of the fastest algorithms for discovering community structures. However, due to the randomness of the LPA, its performance is not suitable for the general purpose of network analysis. In this study, the authors propose an improved version of the label propagation (called AntLP) algorithm using similarity indices and ant colony optimisation (ACO). The AntLP consists of two steps: in the first step, the algorithm assigns weights for edges of the input network using several similarity indices, and in the second step, the AntLP using ACO tries to propagate labels and optimise modularity measure by grouping similar vertices in each community based on the local similarities among the vertices of the network. In order to study the performance of the AntLP, several experiments are conducted on some well-known social network datasets. Experimental simulations demonstrated that the AntLP is better than some community detection algorithms for social networks in terms of modularity, normalised mutual information and running time.

AD-C: a new node anomaly detection based on community detection in social networks

Anomaly detection in social networks as a challenging task has gained great attention. Every unusual behavioural pattern in a social network can be spotted as an anomaly which provides useful information. In this paper, a new method is proposed to identify anomaly based on community detection (AD-C) for the social network graph. Our model is made up of weighting in pre-processing step and three principle processes, including community detection, auxiliary community detection and node filtering. AD-C method offers a flexible framework for anomaly detection, which can be employed in different stages of its related algorithms. The experiments are conducted on two social media datasets, including Facebook and Flickr datasets. Experimental results indicate more efficiency in comparison to other anomaly methods as baselines in terms of the F-score. Also, the results indicate that applying the proposed steps lead to increased accuracy of the community detection methods.

A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks

Community detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network. Detecting communities has been proved to be a NP-hard problem. Therefore, evolutionary based optimization approaches can be used to solve it. But a primary challenge for them is the higher computational complexity when dealing with large scale networks. In this respect, a COMpression based Multi-Objective Evolutionary Algorithm with Decomposition (Com-MOEA/D) for community detection is proposed where the network is first compressed to a much more smaller scale by exploring network topologies. After that, a framework of multi-objective evolutionary algorithm based on decomposition is applied, in which a local information based genetic operator is proposed to speed up the convergence and improve the accuracy of the Com-MOEA/D algorithm. Experimental results on both real world and synthetic networks show the superiority of the proposed method over several state-of-the-art community detection algorithms.

Deep Learning Techniques for Community Detection in Social Networks

Graph embedding is an effective yet efficient way to convert graph data into a low dimensional space. In recent years, deep learning has applied on graph embedding and shown outstanding performance. Adjacency matrix is often taken as the storage data structure of graph. However, there are the problems of insufficient spatial proximity information in adjacency matrix. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial proximity matrix can obtain subspace of the graph which can help convolution neural network easily and quickly extract the spatial localization. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other deep learning methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

Community detection in node-attributed social networks: How structure-attributes correlation affects clustering quality

The majority of parametric community detection (CD) methods working with node-attributed social networks (ASNs) focus on proposing new techniques and rarely pay much attention on the general analysis how ASN properties affect the corresponding CD quality. However, the latter mostly determines the applicability of a CD method in practice. To fulfil the gap, we investigate CD quality dynamics for ASNs with different structure-attributes correlation. The structure-attributes fusion model under consideration is a weight-based one that interpolates between the so-called fixed and non-fixed topology cases and generalizes a wide class of known weight-based models. Within the model, we first theoretically study the influence of correlation on CD quality and secondly illustrate our conclusions on specially constructed synthetic ASNs. Further, we test our conclusions on original and modified real-world ASNs. Our calculations indicate that the presence of correlation noticeably affects CD quality and that the simultaneous usage of network structure and attributes is not always reasonable within the weight-based fusion model under consideration. This makes the common suggestion that "adding attributes to structure leads to better CD results" questionable in certain cases.

SGEQ: A New Social Group Enlarging Query With Size Constraints

In recent years, the problem of k-core has attracted wide-spread research attention due to the popularity of the graph-related applications, such as social network analysis, community detection, and collaboration networks. To efficiently support group-based activity planning, the organizers need to guarantee the size and closeness of the group. Current applications of the k-core only support searching for the maximum k-core group or adding the minimum number of edges to obtain the k-core. However, no research has focused on the problem of enlarging the k-core. In reality, when new tasks arrive, a work team needs to not only recruit new team members based on the requirements but also guarantee sufficient closeness among the members for good cooperation. In this paper, we first formalize a new query, a social group enlarging query with size constraints (SGEQ), which aims to find n users to enlarge a subgraph from a k-core of size m to a (k+Δ)-core of size (m+n) by inserting the minimum number of edges. We prove that the SGEQ problem is NP-hard. To solve this problem, we first propose a novel algorithm, namely, the maximum connection edges algorithm (MCEA), which searches for the inserted vertex that has the most edges with an induced subgraph every time. Then, we develop an optimizing algorithm, namely, the maximum contribution degree algorithm (MCDA), which on average adds a number of edges in the expanded query less than that obtained by the MCEA. Finally, we conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency of the proposed algorithms.

Optimizing community detection in social networks using antlion and K-median

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

Community Detection in Social Networks Using Consensus Clustering

Community detection is one of the most appealing research fields in computer science. Although many different methods have been proposed to cluster the nodes of a graph, none of these methods is complete. Each method has strengths and weaknesses in extracting highly coherent groups of nodes (i.e. communities or clusters). The differences that various methods show are typically due to two main factors: 1) structure of the network they operate on, and 2) the strategy they use to find clusters. Since none of these methods is optimal, it seems a good idea to combine them to take advantage of their strengths while minimizing their weaknesses. In this paper, we present a new approach for the community detection problem by considering an ensemble of community detection methods. We refer to our approach as “Mitra”. The base methods employed in Mitra, use different techniques and strategies to find communities for different applications of network data analysis. Mitra employs some known base community detection methods, receives their results on a network and builds a bipartite network to combine the communities found by the base methods. Then the fast projection technique compresses and summarizes the bipartite network to a new unipartite one. Then we find the communities of the unipartite network in the final step. We evaluate our approach against real and artificial datasets and compare our method with each one of the base methods. Artificial datasets include a diverse collection of large scale benchmark graphs. In this work, the main experimental evaluation function is Normalized Mutual Information (NMI ). We also use several measures to compare the quality and properties of final community structures of the partitions found by all methods.

Overlapping community detection in social networks with Voronoi and tolerance neighborhood-based method

Community detection is typically viewed as a graph clustering problem with early detection algorithms focused on detecting non-overlapping communities and formulating various measures and optimization methods to evaluate the quality of clustering. In recent years, overlapping community detection especially in real-world social networks, has become an important and challenging research area since it introduces the possibility of membership of a vertex in more that one community. Overlapping community detection by its definition implies soft clustering and leads to an ideal application of granular computing methods. In this paper, a hybrid computational geometry approach with Voronoi diagrams and tolerance-based neighborhoods (VTNM) is used to detect overlapping communities in social networks. Voronoi partitioning results in a crisp partition of an Euclidean space and a tolerance relation makes it possible to obtain soft partitions. A Voronoi diagram is a method to partition a plane into regions based on nearness to points in a specific set of sites (seeds). In the VTNM approach, these seeds are used as cores for determining tolerance neighborhoods via a non-transitive binary relation. The intersection of these neighborhoods are used to discover overlapping communities. Our proposed VTNM algorithm was tested with 7 small real-world networks and compared with 11 well-known algorithms. VTNM algorithm shows promising results in terms of the Extended Modularity measure, Average F1-score and Normalized Mutual Information (NMI) measure.

A new algorithm for detecting communities in social networks based on content and structure information

PurposeThe purpose of this paper is to present an algorithm for detecting communities in social networks.Design/methodology/approachThe majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities).FindingsTo validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information.Originality/valueThe experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.

Genetic algorithm-based community detection in large-scale social networks

Communities in social networks are the essential feature which may be considered as a potential parameter in modeling the behavior of the social entities. Detection of communities has attracted a lot of attention in research in social network analysis. It is one of the major challenging problems as it involves high complexity in processing complex web structure. In fact, this problem can be considered as a NP-complete problem in large-scale networks, as this problem is somewhat reducible to the clique problem in graph theory. A number of meta-heuristic algorithms have been proposed to explore the hidden communities. Most of these algorithms have considered the modularity of the network as their objective function. But, the aspect of optimizing the value of modularity is associated with a problem known as resolution limit, where the size of the detected communities depends on the number of edges existing in the network. In this paper, a genetic algorithm-based community detection has been proposed where an efficient single objective function based on similarity matrix has been devised. The similarity index between each pair of nodes has been calculated in a distributed manner over multiple computing nodes. Similarity index proposed in this paper is based on the topological structure of the network. The effectiveness of the proposed approach is examined by comparing the performance with other state-of-the-art community detection algorithms applied over some real-world network datasets.

Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree

Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree

Community detection based on Game Theory

In this paper we describe a novel algorithm based on Game Theory for Community Detection in Online Social Networks. Extending several Game Theoretic approaches that are well established in the literature, we propose a novel algorithm that outperforms the previous ones in terms of computational complexity and efficiency of results, as proved by a number of experiments on standard data sets.

Community Detection in Social Networks Considering Topic Correlations

Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To ach

A three-stage algorithm on community detection in social networks

Detecting communities or clusters of networks is a considerable interesting problem in various fields and interdisciplinary subjects in recent years. Tens of hundreds of methods with significant efforts devoted to community detection in networks, while an open problem in all methods is the unknown number of communities in real networks. It is believed that the central node in a community might be highly surrounded by its neighbors and any two centers of the community reside far from each other, and also believed the similarity among nodes in the same community is larger than the others. Therefore, the local and the global structures’ information shed important light on community detection. In this work, we present a three-stage algorithm to detect communities based on the local and the global information without giving the number of communities beforehand. The three stages include the central nodes identification, the label propagation and the communities combination. The central nodes are identified according to the distance between them larger than the average; the label propagation is to label nodes with the same colors when they reach to the maximum similarity; the communities combination is to merge two communities into one if the increment of the modularity is positive and maximum when the two communities were combined. Experiments and simulation results both on real world and synthetic networks show that the three-stage algorithm possesses well matched properties compared with seven other widely used algorithms, which indicates that three-stage algorithm can be used to detect community in social networks.