GenPerm: A Unified Method for Detecting Non-Overlapping and Overlapping Communities

Chakraborty, Tanmoy; Kumar, Suhansanu; Ganguly, Niloy; Mukherjee, Animesh; Bhowmick, Sanjukta

doi:10.1109/tkde.2016.2554119

Cited by 32 publications

(10 citation statements)

References 45 publications

(91 reference statements)

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“…Although there have been many studies in overlapping community detection so far, the complexity and accuracy of existing overlapping community detection algorithms still need to be improved. erefore, it is necessary to study the detection of overlapping communities [24].…”

Section: Overlapping Community Detectionmentioning

confidence: 99%

Overlapping Community Detection Based on Node Importance and Adjacency Information

Wang

Huang

Fei

et al. 2021

Security and Communication Networks

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Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

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Section: Overlapping Community Detectionmentioning

confidence: 99%

Overlapping Community Detection Based on Node Importance and Adjacency Information

Wang

Huang

Fei

et al. 2021

Security and Communication Networks

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“…Most optimization metrics consider the total number of external neighbors of the vertex. However, in our earlier experiment [Chakraborty et al 2014;Chakraborty 2015;Chakraborty et al 2016b], we empirically demonstrated that a group of vertices are likely to be placed together so long as the number of internal connections is larger than the number of connections to any one single external community. In other words, a vertex which has connections to some external communities, experiences a separate "pull" from each of these external communities.…”

Section: Defining Permanencementioning

confidence: 95%

Permanence and Community Structure in Complex Networks

Chakraborty

Srinivasan

Ganguly

et al. 2016

ACM Trans. Knowl. Discov. Data

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The goal of community detection algorithms is to identify densely-connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily driven to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this paper, we argue that the belongingness of nodes in a community is not uniform. We quantify the degree of belongingness of a vertex within a community by a new vertex-based metric called permanence.The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors: (i) the the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We present the formulation of permanence based on these two quantities. We demonstrate that compared to other existing metrics (such as modularity, conductance and cut-ratio), the change in permanence is more commensurate to the level of perturbation in ground-truth communities. We discuss how permanence can help us understand and utilize the structure and evolution of communities by demonstrating that it can be used to -(i) measure the persistence of a vertex in a community, (ii) design strategies to strengthen the community structure, (iii) explore the core-periphery structure within a community, and (iv) select suitable initiators for message spreading.We further show that permanence is an excellent metric for identifying communities. We demonstrate that the process of maximizing permanence (abbreviated as MaxPerm) produces meaningful communities that concur with the ground-truth community structure of the networks more accurately than eight other popular community detection algorithms. Finally, we provide mathematical proofs to demonstrate the correctness of finding communities by maximizing permanence. In particular, we show that the communities obtained by this method are (i) less affected by the changes in vertex-ordering, and (ii) more resilient to resolution limit, degeneracy of solutions and asymptotic growth of values.

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“…Composite Performance: Figure 4(c) shows performance of all classifiers on families of size ≥ 10 considering both static and dynamic features. For better visualization we adopt the setup used in [25] -for each evaluation metric (such as MiF, MaF, MiAUC, MaAUC), we separately scale the scores of the methods so that the best performing method has a score of 1. The composite performance of a method is the sum of the four normalized scores.…”

Section: Performance Analysismentioning

confidence: 99%

EC2: Ensemble Clustering and Classification for Predicting Android Malware Families

Chakraborty

Pierazzi

Subrahmanian

2020

IEEE Trans. Dependable and Secure Comput.

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GenPerm: A Unified Method for Detecting Non-Overlapping and Overlapping Communities

Cited by 32 publications

References 45 publications

Overlapping Community Detection Based on Node Importance and Adjacency Information

Overlapping Community Detection Based on Node Importance and Adjacency Information

Permanence and Community Structure in Complex Networks

EC2: Ensemble Clustering and Classification for Predicting Android Malware Families

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