Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Edge Weight Regularization over Multiple Graphs for Similarity Learning
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Spectral embedding for dynamic social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Most real-world social network analysis treats edges (relationships) as having different intensities (weights), but the same qualitative properties. We address the problem of modelling edges of qualitatively different types that nevertheless interact with one another. For example, influence flows along friend and colleagues edges differently, but treating the two sets of different kinds of edges as independent graphs surely misses interesting and useful structure. We model the sub graph corresponding to each edge type as a layer, and show how to weight the edges connecting the layers to produce a consistent spectral embedding, including for directed graphs. This embedding can be used to compute social network properties of the combined graph, to predict edges, and to predict edge types. We illustrate with Padgett's dataset of Florentine families in the 15th Century.