A Novel Similarity-based Modularity Function for Graph Partitioning

Feng, Zhidan; Xu, Xiaowei; Yuruk, Nurcan; Schweiger, Thomas A. J.

doi:10.1142/9789812771667_0011

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Similarity-based modularity: [Feng et al 2007] proposed similarity-based modularity which is robust to the groups of nodes in a graph which have dense interconnections. Instead of using edges within communities and between communities as the criteria of partitioning they propose a more general concept, similarity S(i, j), to measure the graph partition quality.…”

Section: Wmentioning

confidence: 99%

Metrics for Community Analysis

et al. 2017

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Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms -disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

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Section: Wmentioning

confidence: 99%

Metrics for Community Analysis

et al. 2017

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“…Step 4: Cluster the nodes of the network into K groups using the partition matrix n K U × and FCM algorithm. Finally, we use improved Modularity proposed by Feng [7] to evaluate the best split.…”

Section: Machine Tool Technology Mechatronics and Information Enginee...mentioning

confidence: 99%

Detecting Overlapping Communities with MDS and Local Expansion FCM

Lin

Xia

et al. 2014

AMM

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Community structure is an important feature to understand structural and functional properties in various complex networks. In this paper, we use Multidimensional Scaling (MDS) to map nodes of network into Euclidean space to keep the distance information of nodes, and then we use topology feature of communities to propose the local expansion strategy to detect initial seeds for FCM. Finally, the FCM are used to uncover overlapping communities in the complex networks. The test results in real-world and artificial networks show that the proposed algorithm is efficient and robust in uncovering overlapping community structure.

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Optimal Island Partition of ADN Based on Complex Network Community Structure

Shen

Peng

et al. 2020

2020 9th International Conference on Power Science and Engineering (ICPSE)

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A Novel Similarity-based Modularity Function for Graph Partitioning

Cited by 3 publications

References 12 publications

Metrics for Community Analysis

Metrics for Community Analysis

Detecting Overlapping Communities with MDS and Local Expansion FCM

Optimal Island Partition of ADN Based on Complex Network Community Structure

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