Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder

Social network alignment, aligning different social networks on their common users, is receiving increasing attention from both academic and industry. Most of the existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks con...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 35; no. 6; pp. 5597 - 5611
Main Authors: Sun, Li, Zhang, Zhongbao, Wang, Feiyang, Ji, Pengxin, Wen, Jian, Su, Sen, Yu, Philip S.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Alignment
Convergence
Decoding
Design optimization
Dynamics
graph neural networks
Heuristic algorithms
network alignment
network embedding
Optimization
Social networking (online)
Social networks
Solids
Tensors
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Social network alignment, aligning different social networks on their common users, is receiving increasing attention from both academic and industry. Most of the existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA , unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3152502