On Multi-query Local Community Detection

Bian, Yuchen; Yan, Yaowei; Cheng, Wei; Wang, Wei; Luo, Dongsheng; Zhang, Xiang

doi:10.1109/icdm.2018.00016

Cited by 22 publications

(18 citation statements)

References 42 publications

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“…Various measures have been proposed, such as conductance [1], k-core [29], k-truss [16], and quasi-cliques [10]. One line of effort utilizes PageRank methods [1,4,14,17], which enjoy provable performance guarantee [1] and are effective in practice [18]. Mahoney et al [24] combine seed information with spectral partitioning and obtain guarantees with respect to conductance.…”

Section: Related Workmentioning

confidence: 99%

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Searching for polarization in signed graphs: a local spectral approach

Han

Ordozgoiti

Gionis

2020

Proceedings of the Web Conference 2020

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Signed graphs have been used to model interactions in social networks, which can be either positive (friendly) or negative (antagonistic). The model has been used to study polarization and other related phenomena in social networks, which can be harmful to the process of democratic deliberation in our society. An interesting and challenging task in this application domain is to detect polarized communities in signed graphs. A number of different methods have been proposed for this task. However, existing approaches aim at finding globally optimal solutions. Instead, in this paper we are interested in finding polarized communities that are related to a small set of seed nodes provided as input. Seed nodes may consist of two sets, which constitute the two sides of a polarized structure.In this paper we formulate the problem of finding local polarized communities in signed graphs as a locally-biased eigen-problem. By viewing the eigenvector associated with the smallest eigenvalue of the Laplacian matrix as the solution of a constrained optimization problem, we are able to incorporate the local information as an additional constraint. In addition, we show that the locally-biased vector can be used to find communities with approximation guarantee with respect to a local analogue of the Cheeger constant on signed graphs. By exploiting the sparsity in the input graph, an indicator-vector for the polarized communities can be found in time linear to the graph size.Our experiments on real-world networks validate the proposed algorithm and demonstrate its usefulness in finding local structures in this semi-supervised manner.

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Section: Related Workmentioning

confidence: 99%

“…It's easy to see they're equivalent. 4 Note that once α * is found, the value β * can be computed by normalizing (L − α * D) † Ds with respect to D.…”

Section: Polarseeds Algorithmmentioning

confidence: 99%

Searching for polarization in signed graphs: a local spectral approach

Han

Ordozgoiti

Gionis

2020

Proceedings of the Web Conference 2020

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“…The process is reapplied on the created clusters (instead of the nodes) to e↵ectuate a bottom-up, scalable, hierarchical clustering. Our clustering approach inherits the flexibility of the underlying centrality model, and is applicable to all variations of graphs (un/directed and un/weighted graph), in contrast to most existing graph clustering algorithms that are designed and applicable to only one variant each [7,[34][35][36][37][38][39][40][41][42]. We present these ideas in Chapter 4.…”

Section: Research Contributions and Thesis Organizationmentioning

confidence: 99%

“…These include [5] which used edge removal algorithm to cluster an undirected graph by recomputing total Markov entropy for every graph instance created by removal of each possible edge, to determine which edge to remove, so that the aggregate entropy decreases the most; [38] which studies multiple chained walker model to allow multiple walkers to explore the network, so that confinement probability of random walks is increased within a local community which contains the query (start of the walk) node; and [39] which was an extension of [38] to support multiple query nodes. A variant of community detection using Markov chain is [41] where a random walk starts from a link and transition probability of Markov chain is used as similarity between a link pair.…”

Section: Clustering Techniquesmentioning

confidence: 99%

“…The subgraph comprises 209 nodes and was extracted as a subgraph of the Bitcoin network. There were 21 initial cluster centres (approximately 10% of the total nodes), instantiated with nodes 145,80,134,90,78,160,164,128,165,137,66,176,93,39,74,109,99,34,129,158,12. After the initial clustering, there were 13 clusters, after the first round of agglomeration there were 7 clusters, and after the second round of agglomeration We next apply this clustering algorithm on a network with ground truth that we used in the previous chapter, namely, the dolphin network [11].…”

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confidence: 99%

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Graph analysis techniques and applications to bitcoin forensics

Phetsouvanh¹

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I would like to express my greatest gratitude to my advisor Assoc. Prof. Anwitaman Datta for his continuous support during my Ph.D. studies. His patient guidance, encouragement, and immense knowledge are precious to me and beyond what words can express. I really appreciate having a supervisor who cares so much about my work, who always responded to my queries promptly, and who did not give up on me despite my slow progress. I would also like to thank all my thesis advisory committee members, especially Assoc. Prof. Frédérique Oggier who guided me with the same care as a co-supervisor. Completing my thesis would have been all the more di cult were it not for her support, encouragement, and for her patiently explaining to me the mathematics and theory on which the ideas of this thesis are founded. I will also like to thank Dr. Alwen Tiu for his guidance and collaboration during the early phase of my PhD. Even though I eventually decided to pursue a di↵erent line of research for my thesis, the experience gained from working with him is invaluable. Finally, I would like to thank the School of Computer Science and Engineering at Nanyang Technological University Singapore for providing me an opportunity to take this study under NTU research scholarship, and for providing financial support to attend a conference and present some of my works.

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Multiple Local Community Detection via High-Quality Seed Identification

Liu

Shao

2020

Web and Big Data

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On Multi-query Local Community Detection

Cited by 22 publications

References 42 publications

Searching for polarization in signed graphs: a local spectral approach

Searching for polarization in signed graphs: a local spectral approach

Graph analysis techniques and applications to bitcoin forensics

Multiple Local Community Detection via High-Quality Seed Identification

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