Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings

Understand

We present Walklets, a novel approach for learning multiscale representations of vertices in a network.

• In contrast to previous works, these representations explicitly encode multiscale vertex relationships in a way that is analytically derivable.
• Walklets generates these multiscale relationships by subsampling short random walks on the vertices of a graph.
• By `skipping' over steps in each random walk, our method generates a corpus of vertex pairs which are reachable via paths of a fixed length.