Locality-Aware Tail Node Embeddings on Homogeneous and Heterogeneous Networks

While the state-of-the-art network embedding approaches often learn high-quality embeddings for high-degree nodes with abundant structural connectivity, the quality of the embeddings for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embeddings. In this article, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the personalization, we propose a locality-aware meta-learning framework, called meta-tail2vec, which learns to learn the regression model for the tail nodes at different localities. Moreover, to address the heterogeneity in nodes and edges on heterogeneous information networks (HINs), we further extend the proposed model and formulate meta-tail2vec+, which is based on a dual-adaptation mechanism to facilitate the locality-aware tail node embeddings on HINs. Finally, we conduct extensive experiments and demonstrate the promising results of both meta-tail2vec and its extension meta-tail2vec+.