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Found 2 papers, 1 papers with code
Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings
This paper studies properties of random walk based node embeddings in the unsupervised setting of discovering hidden block structure in the network, i. e., learning node representations whose cluster structure in Euclidean space reflects their adjacency structure within the network.
Node Embedding via Word Embedding for Network Community Discovery
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data.
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