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Nodes performing different functions in a network have different roles, andthese roles can be gleaned from the structure of the network. Learning latentrepresentations for the roles of nodes helps to understand the network and totransfer knowledge across networks. However, most existing structural embeddingapproaches suffer from high computation and space cost or rely on heuristicfeature engineering. Here we propose RiWalk, a flexible paradigm for learningstructural node representations. It decouples the structural embedding probleminto a role identification procedure and a network embedding procedure. Throughrole identification, rooted kernels with structural dependencies kept are builtto better integrate network embedding methods. To demonstrate the effectivenessof RiWalk, we develop two different role identification methods named RiWalk-SPand RiWalk-WL respectively and employ random walk based network embeddingmethods. Experiments on within-network classification tasks show that ourproposed algorithms achieve comparable performance with other baselines whilebeing an order of magnitude more efficient. Besides, we also conductacross-network role classification tasks. The results show potential ofstructural embeddings in transfer learning. RiWalk is also scalable, making itcapable of capturing structural roles in massive networks.

RiWalk: Fast Structural Node Embedding via Role Identification