NDLI: Understanding the roles of sub-graph features for graph classification: an empirical study perspective

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Understanding the roles of sub-graph features for graph classification: an empirical study perspective

Content Provider	ACM Digital Library
Author	Guo, Ting Zhu, Xingquan
Abstract	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? Motivated by the above concerns, we carry out empirical studies on four real-world graph classification tasks, by using three types of sub-graph features, including frequent sub-graphs, frequent sub-graph selected by using information gain, and random sub-graphs, and by using two types of learning algorithms including Support Vector Machines and Nearest Neighbour. Our experiments show that (1) the discriminative power of sub-graphs varies by their sizes; (2) random sub-graphs have a reasonably good performance; (3) number of sub-graphs is important to ensure good performance; and (4) increasing number of sub-graphs reduces the difference between classifiers built from different sub-graphs. Our studies provide a practical guidance for designing effective sub-graph based graph classification methods.
Starting Page	817
Ending Page	822
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505614
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sub-graph Random feature Graph classification
Content Type	Text
Resource Type	Article

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