State of the art of graph-based data mining

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Frequent pattern mining: current status and future directions (2007, background): “Washio and Motoda (2003) conducted a survey on graph-based data mining.”
Discovering frequent patterns in sensitive data (2010, background): “Patterns (e.g. itemsets [4] and sequential patterns [5] in transactions data, subgraphs [26] in graphical data, episodes in event streams [20]) are local structures in data that capture correlations among a small number of attributes or variables.”
Graph indexing: a frequent structure-based approach (2004, background): “Washioand Motoda [18] has a general introduction on the recent progress of graph-based data mining.”
Optimal assignment kernels for attributed molecular graphs (2005, methodology): “This representation opens the opportunity to use graph mining methods (Washio & Motoda, 2003) to deal with molecular structures.”
Efficient aggregation for graph summarization (2008, methodology): “Washio and Motoda [20] provide an elegant review on this topic.”
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem (2011, background): “Social network concepts, despite their versatility, have come to the forefront especially for these applications.”
Large scale mining and retrieval of visual data in a multimodal context (2008, background): “For a description of the other approaches the interested reader is referred to [Washio and Motoda, 2003].”