Lawrence B. Holder
Abstract
The fast accumulation of graph data is witnessed in a wide range of scientific and commercial domains. Typical graph data include chemical compounds, circuits, biological networks, computer networks, 2D/3D models, XML, RDF and workflows. Graph is regarded as a critical data type for knowledge discovery in bioinformatics, chemical informatics, computer vision, informational retrieval, computer security, semantic web, social science, etc., just to name a few. Unfortunately, due to the lack of graph management and mining tools, it is hard, if not impossible, for users to search and analyze any reasonably large collection of graphs. There is an imminent need for scalable methods for mining and search in graphs and other complex structures.
Index Terms
- Report on the First International Workshop on Mining Graphs and Complex Structures (MGCS'07)
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