Detecting Community Structures in Social Networks by Graph Sparsification

Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals who play important roles in a society such as managers and decision makers. Detection and visualization of these networks that are changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. In this paper, we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks.

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.

Detecting and analyzing community structures is a complex process in social networks. Existing methods of community detection in social networks do not consider both the contents and network structure simultaneously. Users and posts are significant subjects, as they provide wealthy information about social network characteristics, including topics that are of interest to users (topic information) and interactions between users (structure information). Based on the classical Community Overlap Propagation Algorithm (COPRA), we propose an improved Community Overlap Propagation Algorithm based on the Topics and Structure (COPRA-TS), which exploits a comprehensive similarity, and avoids randomness during the label information update process. The proposed COPRA-TS algorithm efficiently filters low-quality data, detects topics of users, computes the topic and structure similarity and utilizes an optimized label information update strategy. Experiments conducted on diverse networks demonstrate that our proposed COPRA-TS algorithm delivers high quality community detection than three notable community detection algorithms.

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.

Recently, several social network analysis algorithms have been optimized by leveraging the community structure commonly found in graphs. Since community structure is fun-damental to these algorithms, storing graphs based on their community structure can significantly enhance the performance of graph algorithms that rely on it for optimization. However, existing graph storage systems do not natively store graphs according to the community structure, which limits their per-formance in retrieving communities. To fill this gap, we pro-pose LSM-Community, a graph storage system inspired by the LSM - Tree design that stores graphs on disk based on their community structure. To dynamically maintain the community structure during graph updates, we present the community-centric dynamic community detection algorithm <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(C^{3}D)$</tex>. Experimental results demonstrate that LSM-Community outperforms other storage systems in classical community discovery tasks (e.g., performing CD on UK-2007 dataset with LSM-Community is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$86.12\times$</tex> faster than Neo4j) while maintaining high performance on classical graph analytic algorithms. This indicates that LSM-Community efficiently supports community discovery and query processing while preserving the performance of classical analytic algorithms,

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.

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.

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.

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

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.

Dynamics on and of networks are two basic processes that drive coevolving networks such as online social networks. The paper investigates the mechanism of coevolving networks using a generalized adaptive voter model based on related work and the homophily principle which is known as a driving mechanism to form community structure in social networks. The proposed model has mechanisms for dynamics on and of coevolving networks, which are node state change via social interactions and link rewiring based on homophily. The numerical simulation of the proposed model reveals that there exists two phase transitions for the parameters adaptability and homophily. This observation implies that the nature of the homophily principle lies in the adaptive mechanism in the proposed model.

Based on the new concept of central network and the node similarity definition, a fast algorithm is proposed for discovering community structures in social networks. Firstly, start from the maximum degree node, then the two nodes with the maximum number of shared neighbors are taken as the initial community. Next, a neighboring node is judged to push into the initial community according to the appearance frequency of the community it belongs to. Finally, the above step is repeated until all the nodes are classified to the proper communities. The experimental results on two real-world networks demonstrate that the proposed algorithm is able to discover community structure from a given network efficiently and accurately without specifying the community number. The algorithm has a time complexity of only O(n).

Nowadays, social networks play an important role in people's daily lives. A social network is a kind of social structure that consists of several nodes that can be individuals or organizations. Most importantly, these nodes are connected by one or more specific types of dependencies, such as friendships or work relationships. Understanding the structure and constituent groups of these networks can give us useful information about the state of society and individuals. In this article, a new solution to solve the problem of social structure detection is provided. Social structure means communities or associations in social networks. An important issue of this context is network graph construction based on objects as nodes and edges as transactions between these objects. Community detection is based on these graphs. The appropriate solution is to identify and create clusters of nodes that have strong connections with each

This study relates the local property of node dominance to local and global properties of a network. Iterative removal of dominated nodes yields a distributed algorithm for computing a core-periphery decomposition of a social network, where nodes in the network core are seen to be essential in terms of network flow and global structure. Additionally, the connected components in the periphery give information about the community structure of the network, aiding in community detection. A number of explicit results are derived, relating the core and periphery to network flow, community structure, and global network structure, which are corroborated by observational results. The method is illustrated using a real world network (DBLP co-authorship network), with ground-truth communities.

When studying dynamic networks, it is often of interest to understand how the community structure of the network changes. However, before studying the community structure of dynamic social networks, one must first collect appropriate network data. In this paper we present a network sampling technique to crawl the community structure of dynamic networks when there is a limitation on the number of nodes that can be queried. The process begins by obtaining a sample for the first time step. In subsequent time steps, the crawling process is guided by community structure discoveries made in the past. Experiments conducted on the proposed approach and certain baseline techniques reveal the proposed approach has at least 35% performance increase in cases when the total query budget is fixed over the entire period and at least 8% increase in cases when the query budget is fixed per time step.