Node, Motif and Subgraph: Leveraging Network Functional Blocks Through Structural Convolution
ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining Barcelona Spain August, 2018, pp. 47-52, 2018.
Networks or graphs provide a natural and generic way for modeling rich structured data. Recent research on graph analysis has been focused on representation learning, of which the goal is to encode the network structures into distributed embedding vectors, so as to enable various downstream applications through off-the-shelf machine learn...More
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