Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder

Li Sun

doi:10.1109/TKDE.2022.3152502

Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder

2022-01-01

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AI

DOI：10.1109/TKDE.2022.3152502

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Abstract

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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.

Keywords:

Social networking (online)

Optimization

Heuristic algorithms

Convergence

Tensors

Decoding

Solids

Social networks

network embedding

network alignment

graph neural networks

Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder

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