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Popular node embedding methods such as DeepWalk follow the paradigm ofperforming random walks on the graph, and then requiring each node to beproximate to those appearing along with it. Though proved to be successful invarious tasks, this paradigm reduces a graph with topology to a set ofsequential sentences, thus omitting global information. To produce global-awarenode embeddings, we propose GlobalWalk, a biased random walk strategy thatfavors nodes with similar semantics. Empirical evidence suggests GlobalWalk cangenerally enhance global awareness of the generated embeddings.

GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling