Inter-Layer Similarity-Based Graph Neural Network for Link Prediction in Social Multiplex Networks

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ABEDINI MARYAM; Shakibian Hadi

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

Inter-Layer Similarity-Based Graph Neural Network for Link Prediction in Social Multiplex Networks

Author(s)

ABEDINI MARYAM | Shakibian Hadi

Keywords

Link prediction

complex networks

multiplex networks

graph neural networks

Abstract

graph neural networks have gained a great popularity in the past few years because they have proven to be useful in many tasks in complex networks, including Link prediction. The complex and multi-layered structure of multiplex networks poses challenges to traditional Link prediction methods. In this study, we propose a new approach based on graph neural networks (GNN) for Link prediction in multiplex networks. In the suggested approach, several adjacency matrices have been aggregated based on measuring the inter-layer similarities and employed in a GNN. The experimental results on benchmark real-world networks show the effectiveness and validity of the method.

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APA: Copy

ABEDINI, MARYAM, & Shakibian, Hadi. (2024). Inter-Layer Similarity-Based Graph Neural Network for Link Prediction in Social Multiplex Networks. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147657/en

Vancouver: Copy

ABEDINI MARYAM, Shakibian Hadi. Inter-Layer Similarity-Based Graph Neural Network for Link Prediction in Social Multiplex Networks. 2024. Available from: https://sid.ir/paper/1147657/en

IEEE: Copy

MARYAM ABEDINI, and Hadi Shakibian, “Inter-Layer Similarity-Based Graph Neural Network for Link Prediction in Social Multiplex Networks,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147657/en

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