research-article

Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks

Authors:

Zhiguo GongAuthors Info & Claims

ACM Transactions on the Web, Volume 15, Issue 4

Article No.: 19, Pages 1 - 22

https://doi.org/10.1145/3457906

Published: 14 June 2021 Publication History

Abstract

Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method (

), which mainly consists of a multiple-neighbor range algorithm (

) and a superposition similarity fluctuation algorithm (

).

introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently,

maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on

and

,

not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that

can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.

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Index Terms

Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 15, Issue 4

November 2021

152 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3465465

Editor:
Brian D. Davison
Lehigh University, USA

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Publication History

Published: 14 June 2021

Accepted: 01 March 2021

Revised: 01 September 2020

Received: 01 April 2019

Published in TWEB Volume 15, Issue 4

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National Natural Science Foundation of China
Nature Science Foundation of Hubei Province
Independent Science and technology Innovation Fund project of Huazhong Agricultural University

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https://doi.org/10.1016/j.ipm.2024.103912
Chen JWang CHong YMi RZhang LWu YWang HZhou Y(2024)A Survey on Anomaly Detection with Few-Shot LearningCognitive Computing - ICCC 202410.1007/978-3-031-77954-1_3(34-50)Online publication date: 29-Nov-2024
https://doi.org/10.1007/978-3-031-77954-1_3
Wang HLiu RShi CChen JFang LLiu SGong Z(2023)Resisting the Edge-Type Disturbance for Link Prediction in Heterogeneous NetworksACM Transactions on Knowledge Discovery from Data10.1145/361409918:2(1-24)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3614099
Wang HLiu GHu P(2023)TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/361022918:1(1-22)Online publication date: 6-Sep-2023
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Qin XZhong CLin H(2023)Community-Based Influence Maximization Using Network Embedding in Dynamic Heterogeneous Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/359454417:8(1-21)Online publication date: 28-Jun-2023
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