Joint Link Prediction and Network Alignment via Cross-graph Embedding

Joint Link Prediction and Network Alignment via Cross-graph Embedding

Xingbo Du, Junchi Yan, Hongyuan Zha

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2251-2257. https://doi.org/10.24963/ijcai.2019/312

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.
Keywords:
Machine Learning: Data Mining
Machine Learning: Semi-Supervised Learning