Evaluation of local community metrics: from an experimental perspective

Ma, Lianhang; Chiew, Kevin; Huang, Hao; He, Qinming

doi:10.1007/s10844-017-0480-5

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Among these surveyed algorithms, the authors have selected some local algorithms that fall into the first four classes, so no section is devoted to local approaches. Moreover, Ma L., Chiew K. et al [25] presented an experimental evaluation of local community metrics. More specifically, they divided the metrics into degree-based and similarity-based ones and conducted experiments on eight different metrics.…”

Section: Related Workmentioning

confidence: 99%

“…et al [22] 2014 Overlapping and disjoint communities in large-scale real networks with ground truth communities Fortunato S., Hric D. [7] 2016 General aspects of community detection problem and discussion on popular methods used Maivizhi R., Sendhilkumar S., Mahalakshmi G. S. [23] 2016 Survey of tools for detection and mining of communities Javed M., Aqib Y., Muhammad Sh. L. et al [24] 2018 Overlapping and disjoint communities in various domains of real life and corresponding applications Ma L., Chiew K. et al [25] 2018 Experimental evaluation of local community detection metrics Dakiche N., Benbouzid-Si T., Fatima S. et al [26] 2019 Tracking community evolution over time in dynamic social networks Dilmaghani S., Brust M., Danoy G. et al [19] 2021 Scheme to investigate the concept of locality at each stage of a community detection workflow Huang X., Chen D., Ren T. et al [27] 2021 Multilayer networks Souravlas S., Sifaleras A., Tsintogianni M. et al [18] 2021 Classification of community detection approaches into: (a) bottom-up, (b) top-down, and (c) data structure-based, concerning social media Meena P., Pawar M. et al [28] 2021 Comparative analysis of community detection methods and Applications used in community detection algorithms Su X., Xue S. et al [29] 2022 Survey on community detection with deep learning stream are unknown, i.e. ∀j ̸ = i, e j is not accessible when e i arrives in stream S.…”

Section: Surveymentioning

confidence: 99%

“…Thus, the time needed to detect a community is proportional to the size of the community [33], [34], [14]. However, mostly in diffusion approaches, their run-time as well as the quality of the detected local communities may depend on some other characteristics of the networks like density and community diameter [68], [25], [39], [100]. For instance, a simple method such as a short random walk may be efficient on a dataset with small diameter communities, but may fail to reach a large part of a community in a sparser graph with a larger diameter.…”

Section: Graph Streams Referencementioning

confidence: 99%

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Local Community Detection: A Survey

2022

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Community detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local community detection is important for numerous real-world applications such as protein interactions and targeted advertising. Since 2005, when the first research paper on local community detection appeared, the literature has been vast and difficult to navigate, as each method works best under certain conditions and assumptions regarding the seed nodes and the identification of their community. For this reason, and motivated by the many real-world applications of local community detection, in this paper we provide a comprehensive overview and taxonomy of local community detection algorithms. There are quite a lot of surveys on community detection that make a compendious reference to local community detection. However, they do not achieve a systematic and comprehensive coverage of this particular field. Since the research area of local community detection is quite extensive, it is necessary to categorize and discuss the various methods, techniques, and assumptions used to address the problem. This survey aims to fill this gap and help researchers get a clear overview of the local community detection problem. To this end, we have also gathered the best documented tools and the most commonly used datasets in the local community detection literature to help researchers identify the tools they can use to prove their methods.

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Section: Related Workmentioning

confidence: 99%

Section: Surveymentioning

confidence: 99%

Section: Graph Streams Referencementioning

confidence: 99%

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Local Community Detection: A Survey

2022

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“…Subsequently, various excellent community detection algorithms that depend on the global information of the network were proposed [7]. However, accessing the global information of a real-world network is sometimes impossible and in some cases, unnecessary [8]. On the one hand, for large-scale or dynamic real-world networks, it is difficult and time-consuming to obtain global information [9].…”

Section: Introductionmentioning

confidence: 99%

A local community detection algorithm based on potential community exploration

Wang

Yang²,

Ding³

et al. 2023

Front. Phys.

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Local community detection aims to detect local communities that have expanded from the given node. Because of the convenience of obtaining the local information of the network and nearly linear time complexity, researchers have proposed many local community detection algorithms to discover the community structure of real-world networks and have obtained excellent results. Most existing local community detection algorithms expand from the given node to a community based on an expansion mechanism that can determine the membership of nodes. However, when determining the membership of neighboring nodes of a community, previous algorithms only considered the impact from the current community, but the impact from the potential communities around the node was neglected. As the name implies, a potential community is a community structure hidden in an unexplored network around a node. This paper gives the definition of potential communities of a node for the first time, that is, a series of connected components consisting of the node’s neighbors that are in the unexplored network. We propose a three-stage local expansion algorithm, named LCDPC, that performs Local Community Detection based on Potential Community exploration. First, we search for a suitable node to replace the given node as the seed by calculating the node importance and the node similarity. Second, we form the initial community by combining the seed and its suitable potential community. Finally, the eligible nodes are selected by comparing the similarities between potential communities and the expanding community and nodes and adding them to the initial community for community expansion. The proposed algorithm is compared with eight state-of-the-art algorithms on both real-world networks and artificial networks, and the experimental results show that the performance of the proposed algorithm is better than that of the comparison algorithms and that the application of potential community exploration can help identify the community structure of networks.

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