A Fast Algorithm to Find Overlapping Communities in Networks

Gregory, Steven

doi:10.1007/978-3-540-87479-9_45

Cited by 134 publications

(79 citation statements)

References 28 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many algorithms have been developed to detect overlapping communities in complex networks, such as CPM [2], CONGA [5], GA-Net+ [9], etc. Among them, CPM is the most famous and widely used.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Genetic Algorithm for Overlapping Community Detection

Cai

Shi

Dong

et al. 2011

Advanced Data Mining and Applications

View full text Add to dashboard Cite

Abstract. There is a surge of community detection on complex network analysis in recent years, since communities often play special roles in the network systems. However, many community structures are overlapping in real word. For example, a professor collaborates with researchers in different fields. In this paper, we propose a novel algorithm to discover overlapping communities. Different from conventional algorithms based on node clustering, our algorithm is based on edge clustering. Since edges usually represent unique relations among nodes, edge clustering will discover groups of edges that have the same characteristics. Thus nodes naturally belong to multiple communities. The proposed algorithm apply a novel genetic algorithm to cluster on edges. A scalable encoding schema is designed and the number of communities can be automatically determined. Experiments on both artificial networks and real networks validate the effectiveness and efficiency of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The node-based overlapping community detection algorithms [1,2,3,4,5,6,7,9], classify nodes of the network directly. The link-based algorithms cluster the edges of network, and map the final link communities to node communities by simply gather nodes incident to all edges within each link communities [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Genetic Algorithm for Overlapping Community Detection

Cai

Shi

Dong

et al. 2011

Advanced Data Mining and Applications

View full text Add to dashboard Cite

show abstract

“…Recently, Palla et al [5] propose the CFinder system to partition complex networks to k-clique communities, where k is a given parameter as clique size. Gregory proposes the CONGA algorithm [1] based on the betweenness score [3] and later extends it to the CONGO algorithm to improve the scalability [6]. He also shows that CONGO provides the same level of performance as CFinder, on synthetic networks.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the fact that they intentionally focus on overlapping community structure makes them find or force overlap even for data without such structure. More importantly, many approaches not only require parameters that are difficult to determine but also their results are very sensitive to parameter settings, e.g., number of communities [1,20], community density [5,18], or size of a local community region [6].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the definition would be different across various domains, or even across different networks of the same domain. Therefore, most proposed approaches [1,6,7,5,4] for overlapping community detection require the user to describe the communities they are looking for by giving parameters, e.g., community size, density range, the number of communities, etc. However, appropriate parameters are usually extremely hard to determine without tedious and repeated testing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ONDOCS: Ordering Nodes to Detect Overlapping Community Structure

Chen

Zaı̈ane

Sander

et al. 2010

Data Mining for Social Network Data

View full text Add to dashboard Cite

Finding communities is an important task for the discovery of underlying structures in social networks. While existing approaches give interesting results, they typically neglect the fact that communities may overlap, with some hub nodes participating in multiple communities. Similarly, most methods cannot deal with outliers, which are nodes that belong to no germane communities. The definition of community is still vague and the criterion to locate hubs or outliers vary. Existing approaches usually require guidance in this regard, specified as input parameters, e.g., the number of communities in the network, without much intuition. Here we present a general community definition and a list of requirements for a community mining metric. We review advantages and disadvantages of existing metrics and propose our new metric to quantify the relation between nodes in a social network. We then use the new metric to build a visual data mining system, which first helps the user to achieve appropriate parameter selection by observing initial data visualizations, then detects overlapping community structure from the network while also excluding outliers. Experiment results verify the scalability and accuracy of our approach on real data networks and show its advantages over existing methods that also consider overlaps. An empirical evaluation of our metric demonstrates superior performance over previous measures.

show abstract

A classification for community discovery methods in complex networks

Coscia

Giannotti

Pedreschi

2011

Statistical Analysis

304

195

View full text Add to dashboard Cite

Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a 'user manual' for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research. 

show abstract

A Fast Algorithm to Find Overlapping Communities in Networks

Cited by 134 publications

References 28 publications

A Novel Genetic Algorithm for Overlapping Community Detection

A Novel Genetic Algorithm for Overlapping Community Detection

ONDOCS: Ordering Nodes to Detect Overlapping Community Structure

A classification for community discovery methods in complex networks

Contact Info

Product

Resources

About