CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
WebOFDAV — navigating and visualizing the Web on-line with animated context swapping
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Statistical synopses for graph-structured XML databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Clustering graphs for visualization via node similarities
Journal of Visual Languages and Computing
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Graphs traditionally have many applications in various areas of computer science. Research in graph-based data mining has recently gained a high level of attraction due to its broad range of applications. Examples include XML documents, web logs, web searches and molecular biology. Most of the approaches used in these applications focus on deriving interesting, frequent patterns from given datasets. Two fundamental questions are, however, ignored; that is, how to derive a graph from a set of objects and how to order nodes according to their relations with others in the graph. In this paper, we provide approaches to building a graph from a given set of objects accompanied by their feature vectors, as well as to ranking nodes in the graph. The basic idea of our ranking approach is to quantify the important role of a node as the degree to which it has direct and indirect relationships with other nodes in a graph. A method for visualising graphs with ranking nodes is also presented. The visual examples and applications are provided to demonstrate the effectiveness of our approaches.