Böhm, Christoph; Kasneci, Gjergji; Naumann, Felix
Proceedings of the Conference on Information and Knowledge Management (CIKM)
Large amounts of graph-structured data are emerging from various avenues, ranging from natural and life sciences to so- cial 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 exploit- ing 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.