Higher-order Spectral Clustering for Heterogeneous Graphs

Understand

Higher-order connectivity patterns such as small induced sub-graphs called graphlets (network motifs) are vital to understand the important components (modules/functional units) governing the configuration and behavior of complex networks.

• Existing work in higher-order clustering has focused on simple homogeneous graphs with a single node/edge type.
• However, heterogeneous graphs consisting of nodes and edges of different types are seemingly ubiquitous in the real-world.
• In this work, we introduce the notion of typed-graphlet that explicitly captures the rich (typed) connectivity patterns in heterogeneous networks.