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arXiv · 1801.09303

HONE: Higher-Order Network Embeddings

Ryan A. Rossi·Nesreen K. Ahmed·Eunyee Koh·Sungchul Kim·Anup Rao·Yasin Abbasi Yadkori
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Abstract

This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network representations. In all cases, HONE outperforms recent embedding methods that are unable to capture higher-order structures with a mean relative gain in AUC of $19\%$ (and up to $75\%$ gain) across a wide variety of networks and embedding methods.

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BibTeXRIS

Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, Yasin Abbasi Yadkori. 2018-01-28. HONE: Higher-Order Network Embeddings. https://arxiv.org/abs/1801.09303

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