Community detection in social media

Physical Review E, 2011

Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.

International journal of innovative technology and exploring engineering, 2019

Advancements in web technologies in conjunction with the advent of social media facilitate online users to share contents and interact on a shared platform. Social media mining allows users to visualize, evaluate, analyze, and extract meaningful patterns and trends over the social network. Numerous methods and algorithms have been presented for the massive investigation of social media data. Community detection over social media is the most attracting field of interest for researchers in the area of social media mining. Community detection is a process of identifying densely connected network nodes and forming a group or community based on the density of interconnection among them. Detection of such communities is very crucial for a variety of applications in order to analyze the social network. This paper provides a brief introduction of social media, social media mining, and highlights prominent and recent research works done in the field of community detection. The paper presents the taxonomy of various algorithms and approaches for community detection over social media. The paper also includes in-depth details of extent community detection methods devised in the literature to detect communities over social media.

BioEssays, 2008

Networks in nature possess a remarkable amount of structure. Via a series of datadriven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman 1 , introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection.

Publishing India Group , 2016

In recent years, online social networks (OSNs) have dramatically expanded in popularity around the world. The rapid growth of OSNs has attracted a large number of researchers to explore and study this popular, ubiquitous, and large-scale service. Community Detection is very important to reveal the structure of social network. Uncovering the community structure of complex networks is helpful for understanding complex systems. Researches on analyzing community structure thus gained growing attention during the past decades.

Computing Research Repository, 2009

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.

2015

Abstract: In real world, there are many networks available such as social networks, biological networks etc. These networks have abundant information stored in them which can be extracted to help the society. So the analysis of complex networks has received a lot of attention from the scientific community during the last decades. Community structure is one of the properties of these networks. Community detection technique is used to find community structure within its complex networks.

2013

Although there are dozens of community detection algorithms, we lack real community structures necessary to create reliable benchmarks for evaluation of such clustering algorithms. We developed a Facebook application to explore three common community detection algorithms. With this tool, we learn from people and real networks to evaluate existing approaches.

Proceedings of the VLDB Endowment, 2015

Revealing the latent community structure, which is crucial to understanding the features of networks, is an important problem in network and graph analysis. During the last decade, many approaches have been proposed to solve this challenging problem in diverse ways, i.e. different measures or data structures. Unfortunately, experimental reports on existing techniques fell short in validity and integrity since many comparisons were not based on a unified code base or merely discussed in theory. We engage in an in-depth benchmarking study of community detection in social networks. We formulate a generalized community detection procedure and propose a procedure-oriented framework for benchmarking. This framework enables us to evaluate and compare various approaches to community detection systematically and thoroughly under identical experimental conditions. Upon that we can analyze and diagnose the inherent defect of existing approaches deeply, and further make effective improvements c...

A social network can be defined as a set of people connected by a set of people. Social network analysis provides both a visual and a mathematical analysis of human relationship. The investigation of the community structure in the social network has been the important issue in many domains and disciplines. Community structure assumes more significance with the increasing popularity of online social network services like Facebook, MySpace, or Twitter. This paper reflects the emergence of communities that occur in the structure of social networks, represented as graphs. We have mainly discussed various community detection algorithms in real world networks in this paper. This paper represents as an overview of the community detection algorithms in social networks.

Social media has followed an exponential graph over the past few years with incorporating features which at one time seemed impossible. The social media has had an enduring effect on the thought process of the general populace. With the diverse nature of the population which take part in the daily chatting, tagging, posting and uploading on the virtual world, the study of such coalesce of communities. This paper aims at the mining and analysis of the communities with focus on the techniques used for the detection process. We discuss four methods of detection, beginning with the node-centric moving on to group centric, then to network centric and concluding with hierarchy centric method of detection. This paper also briefly discusses the applications of community detection in varied fields.