Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2020

Abstract

Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors 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 embedding. In this paper, 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 metatail2vec, which learns to learn the regression model for the tail nodes at different localities. Finally, we conduct extensive experiments and demonstrate the promising results of meta-tail2vec. (Supplemental materials including code and data are available at https://github.com/smufang/meta-tail2vec.)

Keywords

meta-learning, network embedding, tail nodes

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM’20), Virtual Event, Ireland, 2020 October 19-23

First Page

1

Last Page

10

Identifier

10.1145/3340531.3411910

Publisher

ACM

City or Country

Virtual Event, Ireland

Additional URL

https://doi.org/10.1145/3340531.3411910

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