Multiple Local Community Detection via High-Quality Seed Identification

Liu, Jiaxu; Shao, Yingxia; Su, Sen

doi:10.1007/978-3-030-60259-8_4

Cited by 2 publications

(6 citation statements)

References 13 publications

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“…Et al [32],Moradi F. et al [33],Xia S. et al [10],Fanrong M. et al [34],Wang P. et al [35],Hamann M. et al [36],Ding X. et al [37],Tasgin M. et al [38],Xu X. Et al [39],Guo K. et al [8],Luo W. et al [40],Liu J. et al [16],Aghaalizadeh et al [41],Hu Y. et al [42],Ji P. et al [43] Greedy Focus on Quality metric Clauset A. [17],Luo F. et al [44],Chen J. et al [45],Huang J. et al [46],Branting L. et al [47],Wu Y. et al [48],Ngonmang B. et al [49],Kudelka M. et al [50],Ma L. et al [51],Cui W. et al [52] are greedy and non-greedy.…”

Section: Focus On Startingmentioning

confidence: 99%

“…Furthermore, Liu J. et al [16] propose the algorithm HqsMLCD to detect multiple overlapping communities for a given starting node. Their motivation is to define an LCD method that is not sensitive to the position of the seed node but it is sensitive to the local structure of the seed node.…”

Section: ) Greedy Techniquesmentioning

confidence: 99%

“…Since a starting node can belong to multiple communities, in [97] Liu J. et al extend their static algorithm [16] to the dynamic network to solve this problem. In fact, they are the first to propose a method for multiple LCD in dynamic networks, called HqsDMLCD.…”

Section: ) Snapshot Modelmentioning

confidence: 99%

“…Et al [32] LMD local degree central node as seed -Moradi F. et al [33] similarity score to define starting nodes, parameter-free -Xia S. et al [10] ILCDSP uses selection probability to find starting nodes, not very stable -Fanrong M. et al [34] LCD-MC start LCD from maximal clique containing the seed node -Wang P. et al [35] MAGA-LC tightest nodes as seeds -Hamann M. et al [36] start LCD from maximal clique containing the seed node, TCE method, unweighted and weighted networks, overlapping communities Provided Ding X. et al [37] RTLCD use core node as starting one -Tasgin M. et al [38] use of highest score node to start the label propagation method, instability in determining the final communities, weak performance in identifying a reasonable number of communities Provided Xu X. Et al [39] DLCD-CCE expands community centers, can run in a distributed environment, weighted networks -Guo K. et al [8] InfoNode local degree central nodes and Jaccard coefficient to detect starting nodes, overlapping communities, parameter dependent, more than one seeds -Luo W. et al [40] LCDNN nearest nodes with greater centrality as seeds, multiscale local communities -Liu J. et al [16] HqsMLCD multiple communities, higher quality starting nodes -Aghaalizadeh et al [41] use core nodes as starting, weighted networks -Hu Y. et al [42] S-LM local centrality measurements used for selecting starting nodes -Ji P. et al [43] CAELCD finds core nodes as seeds and expand them -PageRank Nibble and others. However, it only supports static graph datasets and not dynamic ones.…”

Section: ) Graphical User Interfacesmentioning

confidence: 99%

“…The network of flavors where nodes represent ingredients and edges are between two ingredients if they have similar flavor compounds, can be used to create new recipes from existing ones by considering the local communities discovered in this network for key ingredients [15]. What is more, LCD is effectively used in collaboration networks where an analyst can determine the membership of a particular person in a working group by detecting local communities [16].…”

Section: Introductionmentioning

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: Focus On Startingmentioning

confidence: 99%

Section: ) Greedy Techniquesmentioning

confidence: 99%

Section: ) Snapshot Modelmentioning

confidence: 99%

Section: ) Graphical User Interfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local Community Detection: A Survey

2022

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Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

Liu

Shao

2021

Data Sci. Eng.

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Local community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we first introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node over static networks. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Since dynamic networks also act an important role in practice, we extend the static HqsMLCD to handle dynamic networks and introduce HqsDMLCD. HqsDMLCD mainly integrates dynamic network embedding and dynamic local community detection into the static one. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms. And our dynamic method HqsDMLCD gets comparable results with the static method on real-world networks.

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Multiple Local Community Detection via High-Quality Seed Identification

Cited by 2 publications

References 13 publications

Local Community Detection: A Survey

Local Community Detection: A Survey

Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

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