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Domain Adaptation on Graphs via Frequency Analysis

Classical machine learning algorithms assume the training and test data to be sampled from the same distribution, while this assumption may be violated in practice. Domain adaptation methods aim to exploit the information available in a source domain in order to improve the performance of classification in a target domain. In this work, we focus on the problem of domain adaptation in graph settings. We consider a source graph with many labeled nodes and aim to estimate the class labels on a target graph with few labeled nodes. Our main assumption about the relation between the two graphs is that the frequency content of the label function has similar characteristics. Building on the recent advances in frequency analysis on graphs, we propose a novel graph domain adaptation algorithm. Experiments on image data sets show that the proposed method performs successfully.