Graph Domain Adaptation with Localized Graph Signal Representations
This addresses the challenge of label scarcity in graph domains, but it is incremental as it builds on existing domain adaptation techniques with a graph-specific approach.
The paper tackles the problem of domain adaptation for graph data by estimating target labels from a source graph with many labels and a target graph with few or no labels, assuming similarity in local label function behavior; it reports satisfactory classification accuracy compared to reference methods.
In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the two graphs. Our assumption about the source and the target domains is that the local behaviour of the label function, such as its spread and speed of variation on the graph, bears resemblance between the two graphs. We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs. In order to efficiently capture the local variation of the label functions on the graphs, spectral graph wavelets are used as the graph bases. Experimentation on various data sets shows that the proposed method yields quite satisfactory classification accuracy compared to reference domain adaptation methods.