A visual canonical adjacency matrix for graphs

Graph data mining algorithms rely on graph canonical forms to compare different graph structures. These canonical form definitions depend on node and edge labels. In this paper, we introduce a unique canonical visual matrix representation that only depends on a graph's topological information,...

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Bibliographic Details
Published in2009 IEEE Pacific Visualization Symposium pp. 89 - 96
Main Authors Hongli Li, Grinstein, G., Costello, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2009
Subjects
Adjacency matrix visualization
Canonical form
Clustering algorithms
Data mining
Data visualization
Filters
History
Mathematics
Stability
Testing
Traveling salesman problems
Tree graphs
Visual graph mining
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Summary:Graph data mining algorithms rely on graph canonical forms to compare different graph structures. These canonical form definitions depend on node and edge labels. In this paper, we introduce a unique canonical visual matrix representation that only depends on a graph's topological information, so that two structurally identical graphs will have exactly the same visual adjacency matrix representation. In this canonical matrix, nodes are ordered based on a breadth-first search spanning tree. Special rules and filters are designed to guarantee the uniqueness of an arrangement. Such a unique matrix representation provides persistence and a stability which can be used and harnessed in visualization, especially for data exploration and studies.
ISBN:1424444047
9781424444045
ISSN:2165-8765
2165-8773
DOI:10.1109/PACIFICVIS.2009.4906842