Understanding the Roles of Sub-graph Features for Graph Classification: An Empirical Study Perspective

Publisher:
ACM
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge manage CIKM, 2013, pp. 817 - 822
Issue Date:
2013-01
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Graph classification concerns the learning of discriminative models, from structured training data, to classify previously unseen graph samples into specific categories, where the main challenge is to explore structural information in the training data to build classifiers. One of the most common graph classification approaches is to use sub-graph features to convert graphs into instance-feature representations, so generic learning algorithms can be applied to derive learning models. Finding good sub-graph features is regarded as an important task for this type of learning approaches, despite that there is no comprehensive understanding on (1) how effective sub-graph features can be used for graph classification? (2) how many sub-graph features are sufficient for good classification results? (3) does the length of the sub-graph features play major roles for classification? and (4) whether some random sub-graphs can be used for graph representation and classification?
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