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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Effectiveness of Representation and Length Variation of Shortest Paths in Graph Classification
verfasst von : Asif Salim, S. S. Shiju, S. Sumitra
Erschienen in: Pattern Recognition and Machine Intelligence
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Abstract
Kernel methods are widely used for the classification of graphs. Different graph kernels have been proposed based on certain properties of graphs such as shortest paths, random walks, subtree patterns, subgraphs etc. Since shortest paths have been used for different graph kernel designs, we make a detailed analysis on the effectiveness of representation and effect of variation in length of shortest paths in classification of the node labeled graphs. We identified that certain modification in their conventional representation and resultant feature extraction gives better results and/or efficient feature representation rather than using them in their trivial definition mode. The effectiveness of resulting representations and length variations are analyzed with their ability to classify labeled graphs with an appropriate graph kernel design using support vector machines.