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Found 3 papers, 2 papers with code
Residual2Vec: Debiasing graph embedding with random graphs
Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs.
Unsupervised embedding of trajectories captures the latent structure of scientific migration
Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation.
Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks.
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