Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2020
Abstract
Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of attribute semantics from different sources, we introduce a mixed model variant which has a twolayer encoder structure. Our experiments on three real-world datasets show that ECAN out-performs previous approaches in both node classification and link prediction tasks.
Keywords
Attributed Networks, Network Embedding, Knowledge Graph
Discipline
Computer Sciences
Research Areas
Data Science and Engineering
Publication
IEEE Big Data 2020 - IEEE International Conference on Big Data, 2020 December 10-13
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.