Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
This addresses the problem of data insufficiency and reliance on user content in social network analytics, offering a scalable solution for applications like user identification, though it is incremental in combining existing graph techniques.
The paper tackles cross-platform account matching by proposing a multi-level graph convolutional network that integrates local network and hypergraph structures, achieving state-of-the-art performance with significant improvements on large-scale real-life social networks.
Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network topology, which brings researchers into an insoluble dilemma of model selection. In this paper, to address this problem, we propose a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner. The proposed method overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information. Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks. Extensive experiments have been conducted on two large-scale real-life social networks. The experimental results demonstrate that the proposed method outperforms the state-of-the-art models with a big margin.