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Network embeddings, which learn low-dimensional representations for eachvertex in a large-scale network, have received considerable attention in recentyears. For a wide range of applications, vertices in a network are typicallyaccompanied by rich textual information such as user profiles, paper abstracts,etc. We propose to incorporate semantic features into network embeddings bymatching important words between text sequences for all pairs of vertices. Weintroduce a word-by-word alignment framework that measures the compatibility ofembeddings between word pairs, and then adaptively accumulates these alignmentfeatures with a simple yet effective aggregation function. In experiments, weevaluate the proposed framework on three real-world benchmarks for downstreamtasks, including link prediction and multi-label vertex classification. Resultsdemonstrate that our model outperforms state-of-the-art network embeddingmethods by a large margin.

Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment