Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
This work addresses alignment inaccuracy in social networks, offering an incremental improvement applicable to various existing methods.
The paper tackles the problem of overly-close user embeddings in social network alignment by introducing pseudo anchors and a meta-learning algorithm to spread embeddings apart, resulting in methods that outperform their counterparts by a large margin, especially with few labeled anchors.
Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that ``overly-close'' user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.