Multi-scale Attributed Node Embedding

ICLR 2020 Benedek RozemberczkiCarl AllenRik Sarkar

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE)... (read more)

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