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AnoEdgePred: A Novel Method for Detecting Anomalous Edges in Social Networks (Record no. 132488)

MARC details
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fixed length control field 03710nam a2200133 4500
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100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Pallavi Raj
245 ## - TITLE STATEMENT
Title AnoEdgePred: A Novel Method for Detecting Anomalous Edges in Social Networks
300 ## - PHYSICAL DESCRIPTION
Extent Pages 205-221
520 ## - SUMMARY, ETC.
Summary, etc. biblio.abstract The social network has grown exponentially, connecting people globally and evolving dynamically. Detecting anomalies within these networks is important as they may indicate malicious activity or error. Traditional algorithms like Isolation Forest and One-Class SVM focus on existing structures and overlook new connections, leading to inadequate handling of dynamic changes. Similarly, recent algorithms like Graph Convolutional Networks (GCNs) and Graph Autoencoders (GAEs), while effective for static graphs, may struggle with computational efficiencies. To address these issues, we introduce AnoEdgePred, a novel method that combines Graph neural networks (GNNs) with link prediction and network analysis techniques to identify anomalies in newly formed connections. The method predicts future edges and analyses their structure to identify anomalies such as stars, cliques, dominant edges, or bottleneck edges. AnoEdgePred provides a comprehensive analysis of edge features and structural deviations, often overlooked by existing methods. We evaluated AnoEdgePred by comparing its performance with four different algorithms across datasets, like Facebook, Enron, Slashdot, Polblogs, and synthetic powerlaw network, using different performance metrics. Results show that AnoEdgePred significantly improves detection accuracy by 5−60% and achieves substantial gain in other metrics like precision, recall, F1-score, and AUC, making it a suitable solution for evolving social networks.<br/><br/><br/>Taylor & Francis Online<br/>Top<br/> Full Article<br/> Figures & data<br/> References<br/> Supplemental<br/> Citations<br/> Metrics<br/> Reprints & Permissions<br/> View PDF(open in a new window)<br/>Share<br/>Formulae display:MathJax Logo?<br/>Abstract<br/>The social network has grown exponentially, connecting people globally and evolving dynamically. Detecting anomalies within these networks is important as they may indicate malicious activity or error. Traditional algorithms like Isolation Forest and One-Class SVM focus on existing structures and overlook new connections, leading to inadequate handling of dynamic changes. Similarly, recent algorithms like Graph Convolutional Networks (GCNs) and Graph Autoencoders (GAEs), while effective for static graphs, may struggle with computational efficiencies. To address these issues, we introduce AnoEdgePred, a novel method that combines Graph neural networks (GNNs) with link prediction and network analysis techniques to identify anomalies in newly formed connections. The method predicts future edges and analyses their structure to identify anomalies such as stars, cliques, dominant edges, or bottleneck edges. AnoEdgePred provides a comprehensive analysis of edge features and structural deviations, often overlooked by existing methods. We evaluated AnoEdgePred by comparing its performance with four different algorithms across datasets, like Facebook, Enron, Slashdot, Polblogs, and synthetic powerlaw network, using different performance metrics. Results show that AnoEdgePred significantly improves detection accuracy by 5−60% and achieves substantial gain in other metrics like precision, recall, F1-score, and AUC, making it a suitable solution for evolving social networks
654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS
Subject <a href="Edge prediction">Edge prediction</a>
-- <a href="Graph Neural Networks (GNNs)">Graph Neural Networks (GNNs)</a>
-- <a href="Link prediction">Link prediction</a>
-- <a href="Network analysis techniques">Network analysis techniques</a>
-- <a href="Social network analysis">Social network analysis</a>
773 0# - HOST ITEM ENTRY
Host Biblionumber 80270
Host Itemnumber 113442
Place, publisher, and date of publication New Delhi IETE
Title IETE Technical Review
International Standard Serial Number 0256-4602
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Journal Article
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    Dewey Decimal Classification     SNDT Juhu SNDT Juhu 21/08/2025   JP864.3 21/08/2025 21/08/2025 Journal Article