Spectral Co-Clustering for Dynamic Bipartite Graphs

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A common task in many domains with a temporal aspect involves identifying and tracking clusters over time. Often dynamic data will have a feature-based representation. In some cases, a direct mapping will exist for both objects and features over time. But in many scenarios, smaller subsets of objects or features alone will persist across succes- sive time periods. To address this issue, we propose a dynamic spectral co-clustering method for simultaneously clustering objects and features over time, as represented by successive bipartite graphs. We evaluate the method on a benchmark text corpus and Web 2.0 bookmarking data.

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