Graph GOSPA Similarity Function for Gaussian Process Regression on Graphs
Keywords: Gaussian Process, Graph Matching, Molecular Graph
Abstract: In this paper, we propose a similarity function between graphs based on a mathematically principled metric for graphs of different sizes: the graph generalised optimal subpattern assignment (GOSPA) metric. The similarity function is based on an optimal assignment between nodes and has an interpretable meaning in terms of similarity for node attribute error, number of unassigned nodes, and number of edge mismatches. The proposed similarity function is computable in polynomial time. We also propose its use in Gaussian processes (GPs) for graphs to predict molecular properties. Experimental results show the benefits of the proposed GP model compared to other GP baselines.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 10924
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