Content Patterns in Topic-Based Overlapping Communities

Abstract

Understanding the underlying community structure is an important challenge in social network analysis. Most state-of-the-art algorithms only consider structural properties to detect disjoint subcommunities and do not include the fact that people can belong to more than one community and also ignore the information contained in posts that users have made. To tackle this problem, we developed a novel methodology to detect overlapping subcommunities in online social networks and a method to analyze the content patterns for each subcommunities using topic models. This paper presents our main contribution, a hybrid algorithm which combines two different overlapping sub-community detection approaches: the first one considers the graph structure of the network (topology-based subcommunities detection approach) and the second one takes the textual information of the network nodes into consideration (topic-based subcommunities detection approach). Additionally we provide a method to analyze and compare the content generated. Tests on real-world virtual communities show that our algorithm outperforms other methods.

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

“…For an example, data propagation is subjected by the group structure in the online social community [9][10][11][12]. The migration of humans or birds in the network have the capacity to spread the diseases across the globe [12].…”

“…The migration of humans or birds in the network have the capacity to spread the diseases across the globe [12]. The structure of grid community predicts the electricity supply system in fail propagation.…”

“…The structure of grid community predicts the electricity supply system in fail propagation. The community structure is found by measuring the electric circuit with efficient layout [12,13]. First source is the data regarding their attributes and the objects [14,15].…”

“…First source is the data regarding their attributes and the objects [14,15]. We can identify the related objects and it belongs to which community using the users, authors, and known properties of proteins, publication history or social network profiles [11][12][13][14]. In this user forms the protein interact, friendships, and authors team [15].…”

“…The networks are becoming wider and wider since it is the period of information explosion. Thus, we required many effective community detection algorithms for analyzing the networks with millions of vertices [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”