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Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction
Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs.
Multi-View Graph Embedding Using Randomized Shortest Paths
This data is well represented by multi-view graphs, which consist of several distinct sets of edges over the same nodes.
Faster Clustering via Non-Backtracking Random Walks
VEC employs a novel application of the state-of-the-art word2vec model to embed a graph in Euclidean space via random walks on the nodes of the graph.
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