A Visual Canonical Adjacency Matriz 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 graphs 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.
Visual graph mining Canonical form Adjacency matriz visualization
Hongli Li Georges Grinstein Loura Costello
Pfizer Research Technology Center University of Massachusetts Lowell
国际会议
IEEE Pacific Visualization Symposium 2009(2009 IEEE太平洋可视化研讨会)
北京
英文
89-96
2009-04-29（万方平台首次上网日期，不代表论文的发表时间）