Probabilistic prototype models for attributed graphs

TL;DR

This paper introduces probabilistic models for classifying attributed graphs by defining random attributed graphs with node and edge annotations as random variables, using likelihood-based features for robust and efficient classification.

Contribution

It presents a novel probabilistic framework for attributed graph classification using random attributed graphs and likelihood-based features, enhancing robustness and speed.

Findings

Fast classification method

Robust to noise

Effective likelihood-based features

Abstract

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