2011/09/22 by Shankar Srinivasan, S. Deepak Srinivasan, Srinivasan, S. Deepak +2
Computer Science · #Bayesian Modeling and Causal Inference #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning and Data Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1109.4744
arxiv created 2011/09/22 · openalex publication_date 2011/09/22 · arxiv updated 2011/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.