research-article

Digger: Detect Similar Groups in Heterogeneous Social Networks

Authors:

Yadong ZhouAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 1

Article No.: 2, Pages 1 - 27

https://doi.org/10.1145/3267106

Published: 19 December 2018 Publication History

Abstract

People participate in multiple online social networks, e.g., Facebook, Twitter, and Linkedin, and these social networks with heterogeneous social content and user relationship are named as heterogeneous social networks. Group structure widely exists in heterogeneous social networks, which reveals the evolution of human cooperation. Detecting similar groups in heterogeneous networks has a great significance for many applications, such as recommendation system and spammer detection, using the wealth of group information. Although promising, this novel problem encounters a variety of technical challenges, including incomplete data, high time complexity, and ground truth. To address the research gap and technical challenges, we take advantage of a ratio-cut optimization function to model this novel problem by the linear mixed-effects method and graph spectral theory. Based on this model, we propose an efficient algorithm called Digger to detect the similar groups in the large graphs. Digger consists of three steps, including measuring user similarity, construct a matching graph, and detecting similar groups. We adopt several strategies to lower the computational cost and detail the basis of labeling the ground truth. We evaluate the effectiveness and efficiency of our algorithm on five different types of online social networks. The extensive experiments show that our method achieves 0.693, 0.783, and 0.735 in precision, recall, and F1-measure, which significantly surpass the state-of-arts by 24.4%, 15.3%, and 20.7%, respectively. The results demonstrate that our proposal can detect similar groups in heterogeneous networks effectively.

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Cited By

Yu ZXu WSukjairungwattana PYu L(2023)A Meta-Analysis of Facebook-Assisted Learning Outcomes in Different Countries or RegionsInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.31931218:1(1-18)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.4018/IJITWE.319312
Palubeckis G(2022)Metaheuristic approaches for ratio cut and normalized cut graph partitioningMemetic Computing10.1007/s12293-022-00365-w14:3(253-285)Online publication date: 29-Apr-2022
https://doi.org/10.1007/s12293-022-00365-w
Guzzi PSalerno ETradigo GVeltri P(2020)Extracting Dense and Connected Communities in Dual Networks: An Alignment Based AlgorithmIEEE Access10.1109/ACCESS.2020.30209248(162279-162289)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3020924

Index Terms

Digger: Detect Similar Groups in Heterogeneous Social Networks
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 1

February 2019

340 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3301280

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 December 2018

Accepted: 01 August 2018

Revised: 01 July 2018

Received: 01 January 2018

Published in TKDD Volume 13, Issue 1

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Open Project Program of the National Laboratory of Pattern Recognition
National Natural Science Foundation of China

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Cited By

Yu ZXu WSukjairungwattana PYu L(2023)A Meta-Analysis of Facebook-Assisted Learning Outcomes in Different Countries or RegionsInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.31931218:1(1-18)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.4018/IJITWE.319312
Palubeckis G(2022)Metaheuristic approaches for ratio cut and normalized cut graph partitioningMemetic Computing10.1007/s12293-022-00365-w14:3(253-285)Online publication date: 29-Apr-2022
https://doi.org/10.1007/s12293-022-00365-w
Guzzi PSalerno ETradigo GVeltri P(2020)Extracting Dense and Connected Communities in Dual Networks: An Alignment Based AlgorithmIEEE Access10.1109/ACCESS.2020.30209248(162279-162289)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3020924

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