HONE: Higher-Order Network Embeddings

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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Cited by in corpus (5)

• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
• On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
• Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks
• Higher-Order Networks Representation and Learning: A Survey
• Heterogeneous Network Motifs