Improved semantic-aware network embedding with fine-grained word alignment

Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. We propose to incorporate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. We introduce a word-by-word alignment framework that measures the compatibility of embeddings between word pairs, and then adaptively accumulates these alignment features with a simple yet effective aggregation function. In experiments, we evaluate the proposed framework on three real-world benchmarks for downstream tasks, including link prediction and multi-label vertex classification. Results demonstrate that our model outperforms state-of-the-art network embedding methods by a large margin.
Original languageEnglish (US)
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Print)9781948087841
StatePublished - Jan 1 2020
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09


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