A Local Seeding Algorithm for Community Detection in Dynamic Networks

Hu, Yanmei; Ying-xi, Zhang; Wang, Xiabing; Wu, Jing

doi:10.1007/978-3-030-95408-6_8

Cited by 2 publications

(6 citation statements)

References 29 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%

“…Finally, Hu Y. et al [42] propose three hybrid local centrality measures which combine degree and denseness of node neighborhood to evaluate how good a node is as a seed, and then apply them to a local algorithm for selecting seeds in networks. They focus on local seeding because of its efficiency.…”

Section: ) Greedy Techniquesmentioning

confidence: 99%

“…Finally, based on static local seeding, Hu Y. et al [42] propose a dynamic local seeding algorithm to handle dynamic networks by considering only the nodes affected by network changes. More precisely, the authors suggest a technique to update the centrality values of nodes involved in network change.…”

Section: ) Temporal Smoothnessmentioning

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 random-walk based method proposed by Shi P. et al [88] performs very well compared to other state-of-the-art methods. The authors also provide the source code of the proposed approach and conducted experiments -Adaptation of the static L-metric to dynamic -Zakrzewska A. et al [11] focuses on maintaining the community centered around the seed -Gao W. et al [91] EvoLeaders leader nodes -Nathan E. et al [92] combination of Katz and PageRank centrality, requires a priori knowledge about the size of the community -DiTursi D. et al [93] PHASR temporal conductance measure, weighted -Javadi S. et al [94] leader nodes, degree centrality -Guo K. et al [95] local fitness, node contribution, DyCDNC method -Papadopoulos A. et al [96] -Extension of DiTursi D. et al [93] PHASR algorithm to conform to the Apache Spark engine distributed processing standard -Liu J. et al [97] HqsDMLCD extension of Liu J. et al [16] to dynamic networks, first work for dynamic local multiple communities - [98] LDM-CET Personalized PageRank approach, overlapping -Rigi M. et al [99] method inspired by geometric active contours -Fu D. et al [100] L-MEGA motif-based method, multi-linear PageRank vector by edge filtering -Hu Y. et al [42] D-LM method extension of their static version, not suitable when many nodes are affected by changes -…”

Section: Insights and Future Directionsmentioning

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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