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This contribution proposes a new approach towards developing a class ofprobabilistic methods for classifying attributed graphs. The key concept israndom attributed graph, which is defined as an attributed graph whose nodesand edges are annotated by random variables. Every node/edge has two randomprocesses associated with it- occurence probability and the probabilitydistribution over the attribute values. These are estimated within the maximumlikelihood framework. The likelihood of a random attributed graph to generatean outcome graph is used as a feature for classification. The proposed approachis fast and robust to noise.

Probabilistic prototype models for attributed graphs