Community detection in Social Media

Abstract

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory Responsible editor: 123 516 S. Papadopoulos et al.requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.

Signed Network Embedding in Social Media

“…It has been proven to be useful in many tasks of network analysis such as link prediction [1], community detection [2], node classification [3] and visualization [6]. Data sparsity is the common problem faced by these tasks.…”

“…The increasing availability of large-scale social media networks has greatly advanced social network analysis; and network embedding, which aims to learn lowdimensional vector representations for nodes, has been proven to be useful in many tasks of social network analysis such as link prediction [1], community detection [2], node classification/clustering [3,4,5] and visualization [6]. The vast majority of existing algorithms have been designed for social networks without sign or only with positive links.…”

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

“…For that, the reader is referred to several authoritative survey articles that are already available (Fortunato, 2010;Papadopoulos et al, 2012). The material presented in this paper will introduce the community detection problem with all the necessary background.…”

“…Since then, such community-level organization has been discovered and studied in a wide variety of real world networks, both within nature-built and human-built systems. These include (but not limited to) the Internet, social networks such as Facebook and Twitter (Papadopoulos et al, 2012), C. Elegans neural system (Watts and Strogatz, 1998), protein interaction networks (Girvan and Newman, 2002), electric power grid (Newman, 2003), dolphin communication network (Connor et al, 1999), collaboration networks (Newman, 2003), customer preference databases (Reddy et al, 2002), and climate variability networks (Steinhaeuser et al, 2011). In these systems, discovering the community structures within networks have proved to be a critical step in advancing the knowledge and understanding of the underlying system and its functions.…”

“…An elaborate discussion on all their individual definitions is out of scope for this article, and a curious reader is referred to some of the authoritative reviews in literature (Fortunato, 2010;Papadopoulos et al, 2012).…”