research-article

On Integrating Network and Community Discovery

Authors:

Charu Aggarwal,

Jiawei HanAuthors Info & Claims

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Pages 117 - 126

https://doi.org/10.1145/2684822.2685323

Published: 02 February 2015 Publication History

Abstract

The problem of community detection has recently been studied widely in the context of the web and social media networks. Most algorithms for community detection assume that the entire network is available for online analysis. In practice, this is not really true, because only restricted portions of the network may be available at any given time for analysis. Many social networks such as Facebook have privacy constraints, which do not allow the discovery of the entire structure of the social network. Even in the case of more open networks such as Twitter, it may often be challenging to crawl the entire network from a practical perspective. For many other scenarios such as adversarial networks, the discovery of the entire network may itself be a costly task, and only a small portion of the network may be discovered at any given time. Therefore, it can be useful to investigate whether network mining algorithms can integrate the network discovery process tightly into the mining process, so that the best results are achieved for particular constraints on discovery costs. In this context, we will discuss algorithms for integrating community detection with network discovery. We will tightly integrate with the cost of actually discovering a network with the community detection process, so that the two processes can support each other and are performed in a mutually cohesive way. We present experimental results illustrating the advantages of the approach.

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Cited By

Wu ZWu Z(2020)An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive WeightsIEEE Access10.1109/ACCESS.2020.29723338(31171-31179)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2972333
Gasparetti FSansonetti GMicarelli A(2020)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-3Online publication date: 25-Nov-2020
https://doi.org/10.1007/s10489-020-01962-3
Wang HWang FXu KWang HWang FXu K(2020)Clustering of Online Social Network GraphsModeling Information Diffusion in Online Social Networks with Partial Differential Equations10.1007/978-3-030-38852-2_4(27-41)Online publication date: 17-Mar-2020
https://doi.org/10.1007/978-3-030-38852-2_4
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Index Terms

On Integrating Network and Community Discovery
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

February 2015

482 pages

ISBN:9781450333177

DOI:10.1145/2684822

General Chairs:
Xueqi Cheng
ICT, Chinese Academy of Sciences, China
,
Hang Li
Huawei Technologies, China
,
Program Chairs:
Evgeniy Gabrilovich
Google, USA
,
Jie Tang
Tsinghua University, China

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 February 2015

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WSDM 2015: Eighth ACM International Conference on Web Search and Data Mining

February 2 - 6, 2015

Shanghai, China

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WSDM '15 Paper Acceptance Rate 39 of 238 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Wu ZWu Z(2020)An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive WeightsIEEE Access10.1109/ACCESS.2020.29723338(31171-31179)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2972333
Gasparetti FSansonetti GMicarelli A(2020)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-3Online publication date: 25-Nov-2020
https://doi.org/10.1007/s10489-020-01962-3
Wang HWang FXu KWang HWang FXu K(2020)Clustering of Online Social Network GraphsModeling Information Diffusion in Online Social Networks with Partial Differential Equations10.1007/978-3-030-38852-2_4(27-41)Online publication date: 17-Mar-2020
https://doi.org/10.1007/978-3-030-38852-2_4
Wang HWang FXu KWang HWang FXu K(2020)IntroductionModeling Information Diffusion in Online Social Networks with Partial Differential Equations10.1007/978-3-030-38852-2_1(1-2)Online publication date: 17-Mar-2020
https://doi.org/10.1007/978-3-030-38852-2_1
Wu Z(2019)DwfwFcm: An effective fuzzy c-means clustering framework considering the different data weights and feature weightsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-190440(1-9)Online publication date: 7-Aug-2019
https://doi.org/10.3233/JIFS-190440
Cui ZPark NChakraborty T(2019)Incremental community discovery via latent network representation and probabilistic inferenceKnowledge and Information Systems10.1007/s10115-019-01422-6Online publication date: 15-Nov-2019
https://doi.org/10.1007/s10115-019-01422-6
Deng XNie ZZhai J(2019)Probabilistic Graphical Model Based Highly Scalable Directed Community Detection AlgorithmTrends and Applications in Knowledge Discovery and Data Mining10.1007/978-3-030-26142-9_28(322-340)Online publication date: 12-Sep-2019
https://doi.org/10.1007/978-3-030-26142-9_28
Sarkar SKarn AMukherjee ABhowmick S(2019)An Empirical Study of the Effect of Noise Models on Centrality MetricsDynamics On and Of Complex Networks III10.1007/978-3-030-14683-2_1(3-21)Online publication date: 14-May-2019
https://doi.org/10.1007/978-3-030-14683-2_1
Sarkar SKumar SBhowmick SMukherjee A(2018)Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and ReliabilityMachine Learning Techniques for Online Social Networks10.1007/978-3-319-89932-9_7(135-154)Online publication date: 31-May-2018
https://doi.org/10.1007/978-3-319-89932-9_7
Duong VKhan KLee Y(2017)Top-k frequent induced subgraph mining on a sliding window using samplingProceedings of the 11th International Conference on Ubiquitous Information Management and Communication10.1145/3022227.3022242(1-7)Online publication date: 5-Jan-2017
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