Optimal local community detection in social networks based on density drop of subgraphs

Qi, Xingqin; Tang, Wenliang; Wu, Yirong; Guo, Guodong; Fuller, Edgar; Zhang, Cun-Quan

doi:10.1016/j.patrec.2013.09.008

Cited by 45 publications

(29 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed algorithms are examined using Tiernan and Weinblatt model [14]. The RemoveN method detects all the theoretically possible circuits but repetition circuits in fully connected graphs.…”

Section: Methodsmentioning

confidence: 99%

Social Network Community Detection Using Strongly Connected Components

Lee

Kim

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The hasty information growth of social network poses the information searching efficiency trials for network mining research. Social network graphs and web graphs are huge sources of highly densely connected hypertext links so that the social networks can be described by a directed graph. This kind of network has inherent structural characteristics such as overly expanded, duplicated, connectedness, and circuit paths, which could generate serious challenges for structured searching for sub-network isomorphism and community detection. In this paper, an efficient searching algorithm is suggested to discover social network communities for overcoming the circuit path issue embedded in the social network environment. Experimental results indicate that the proposed algorithm has better performance than the traditional circuit searching algorithms in terms of the time complexity as well as performance criteria.

show abstract

Section: Methodsmentioning

confidence: 99%

Social Network Community Detection Using Strongly Connected Components

Lee

Kim

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The D value method achieves better performance for community detection. Objective optimization seems to be an effective approach for community detection [2,5,23,28,34,37]. However, there is a serious resolution scale problem for this strategy.…”

Section: Related Workmentioning

confidence: 99%

A framework of mapping undirected to directed graphs for community detection

Sun

Gao

2015

Information Sciences

View full text Add to dashboard Cite

“…Community structure is one of the most important properties of complex systems, and community detection is an effective approach to study this property. The goal of detecting community structure is to get an appropriate classification where the links to the nodes with the community are dense, while the links to the nodes out of the community are sparse [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Divisive Algorithm for Community Detection Based on Weak Link and Link-Break Strategy

Ding¹,

Zhang²,

Yang³

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Divisive algorithms are widely used for community detection. A common strategy of divisive algorithms is to remove the external links which connect different communities so that communities get disconnected from each other. Divisive algorithms have been investigated for several decades but some challenges remain unsolved: (1) how to efficiently identify external links, (2) how to efficiently remove external links, and (3) how to end a divisive algorithm with no help of predefined parameters or community definitions. To overcome these challenges, we introduced a concept of the weak link and autonomous division. The implementation of the proposed divisive algorithm adopts a new link-break strategy similar to a tug-of-war contest, where communities act as contestants and weak links act as breakable ropes. Empirical evaluations on artificial and real-world networks show that the proposed algorithm achieves a better accuracy-efficiency trade-off than some of the latest divisive algorithms.

show abstract

Optimal local community detection in social networks based on density drop of subgraphs

Cited by 45 publications

References 16 publications

Social Network Community Detection Using Strongly Connected Components

Social Network Community Detection Using Strongly Connected Components

A framework of mapping undirected to directed graphs for community detection

An Autonomous Divisive Algorithm for Community Detection Based on Weak Link and Link-Break Strategy

Contact Info

Product

Resources

About