  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.
