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» RASCAL: Calculation of Graph Similarity using Maximum Common...
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CJ
2002
61views more  CJ 2002»
13 years 10 months ago
RASCAL: Calculation of Graph Similarity using Maximum Common Edge Subgraphs
John W. Raymond, Eleanor J. Gardiner, Peter Willet...
ISDA
2009
IEEE
14 years 5 months ago
Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Dete
—Protein binding sites are often represented by means of graphs capturing their most important geometrical and physicochemical properties. Searching for structural similarities a...
Imen Boukhris, Zied Elouedi, Thomas Fober, Marco M...
JGAA
2007
135views more  JGAA 2007»
13 years 10 months ago
Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Dat
Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Det...
Donatello Conte, Pasquale Foggia, Mario Vento
CIKM
2009
Springer
14 years 5 months ago
Learning to rank graphs for online similar graph search
Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chem...
Bingjun Sun, Prasenjit Mitra, C. Lee Giles
PKDD
2010
Springer
235views Data Mining» more  PKDD 2010»
13 years 8 months ago
Online Structural Graph Clustering Using Frequent Subgraph Mining
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or t...
Madeleine Seeland, Tobias Girschick, Fabian Buchwa...