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While most network embedding techniques model the relative positions of nodesin a network, recently there has been significant interest in structuralembeddings that model node role equivalences, irrespective of their distancesto any specific nodes. We present PhUSION, a proximity-based unified frameworkfor computing structural and positional node embeddings, which leverageswell-established methods for calculating node proximity scores. Clarifying apoint of contention in the literature, we show which step of PhUSION producesthe different kinds of embeddings and what steps can be used by both. Moreover,by aggregating the PhUSION node embeddings, we obtain graph-level features thatmodel information lost by previous graph feature learning and kernel methods.In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35methods, we systematically reveal successful design choices for node andgraph-level machine learning with embeddings.

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding