GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction
This addresses the problem of linking user accounts across social media for applications like data integration, but it is incremental as it builds on existing methods to handle low-quality data.
The paper tackles the matching collision problem in anchor link prediction across social networks by constructing a matching graph based on local structure consistency and using graph convolution networks with mini-batch strategy. Experimental results on three real scenarios show improvements in prediction accuracy and efficiency.
Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem \textit{anchor link prediction} is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on learning matching function with explicit or implicit features on observed user data. However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice. In this paper, we explore local structure consistency and then construct a matching graph in order to circumvent matching collisions. Furthermore, we propose graph convolution networks with mini-batch strategy, efficiently solving anchor link prediction on matching graph. The experimental results on three real application scenarios show the great potentials of our proposed method in both prediction accuracy and efficiency. In addition, the visualization of learned embeddings provides us a qualitative way to understand the inference of anchor links on the matching graph.