Latent topics in graph-structured data

Authors:
Christoph Böhm;Gjergji Kasneci;Felix Naumann
Affiliations:
Hasso Plattner Institute, Potsdam, Germany;Hasso Plattner Institute, Potsdam, Germany;Hasso Plattner Institute, Potsdam, Germany
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

Large amounts of graph-structured data are emerging from various avenues, ranging from natural and life sciences to social and semantic web communities. We address the problem of discovering subgraphs of entities that reflect latent topics in graph-structured data. These topics are structured meta-information providing further insights into the data. The presented approach effectively detects such topics by exploiting only the structure of the underlying graph, thus avoiding the dependency on textual labels, which are a scarce asset in prevalent graph datasets. The viability of our approach is demonstrated in experiments on real-world datasets.