Metrics for Community Analysis: A Survey

Chakraborty, Tanmoy; Dalmia, Ayushi; Mukherjee, Animesh; Ganguly, Niloy

doi:10.48550/arxiv.1604.03512

Cited by 2 publications

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“…Also, we discuss some popular classes of algorithms and give advice on their usage. More info on network clustering can be found in several review articles (Chakraborty et al, 2016;Coscia et al, 2011;Malliaros and Vazirgiannis, 2013;Parthasarathy et al, 2011;Porter et al, 2009;Schaeffer, 2007;Xie et al, 2013).…”

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confidence: 99%

Community detection in networks: A user guide

2016

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Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.

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confidence: 99%

Community detection in networks: A user guide

2016

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“…Evaluation metrics. For networks with ground-truth communities, the most used evaluation metrics are bi-matching F1 score and Jaccard score [4,8,14,41]. Given 𝑀 ground truth communities { 𝐶 𝑗 } and 𝑁 generated communities { Ĉ𝑖 }, we compute scores as:…”

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confidence: 99%

CLARE: A Semi-supervised Community Detection Algorithm

Wu,

Xiong,

Zhang

et al. 2022

Preprint

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Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a particular kind of community. This limits its applicability in real-world networks, e.g., distinguishing fraud groups from normal ones in transaction networks. Recently, semi-supervised community detection emerges as a solution. It aims to seek other similar communities in the network with few labeled communities as training data. Existing works can be regarded as seed-based: locate seed nodes and then develop communities around seeds. However, these methods are quite sensitive to the quality of selected seeds since communities generated around a mis-detected seed may be irrelevant. Besides, they have individual issues, e.g., inflexibility and high computational overhead. To address these issues, we propose CLARE, which consists of two key components, Community Locator and Community Rewriter.Our idea is that we can locate potential communities and then refine them. Therefore, the community locator is proposed for quickly locating potential communities by seeking subgraphs that are similar to training ones in the network. To further adjust these located communities, we devise the community rewriter. Enhanced by deep reinforcement learning, it suggests intelligent decisions, such as

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Metrics for Community Analysis: A Survey

Cited by 2 publications

References 0 publications

Community detection in networks: A user guide

Community detection in networks: A user guide

CLARE: A Semi-supervised Community Detection Algorithm

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