Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

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Dominique Perrault-joncas, Marina Meila


This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algo- rithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data.