Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Symmetrizations for clustering directed graphs
Proceedings of the 14th International Conference on Extending Database Technology
A quantitative comparison of stress-minimization approaches for offline dynamic graph drawing
GD'11 Proceedings of the 19th international conference on Graph Drawing
A framework for exploring organizational structure in dynamic social networks
Decision Support Systems
Global Similarity in Social Networks with Typed Edges
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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The interactions in real-world social networks change over time. Dynamic social network analysis aims to understand the structures in networks as they evolve, building on static analysis techniques but including variation. Working directly with the graphs that represent social networks is difficult, and it has become common to use spectral techniques that embed graphs in a geometry and then work with the geometry instead. We extend such spectral techniques to dynamically changing data by binding network snapshots at different times into a single directed graph structure in a way that keeps structures aligned. This global network can then be embedded. Pairwise similarity, as well as community and cluster structures can be tracked over time, and the idea of the trajectory of a node across time becomes meaningful. We illustrate the approach using a real-world dataset, the Caviar drug-trafficking network.