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The choice of\na distance or similarity metric is, however, not trivial and can be highly\ndependent on the application at hand. In this work, we propose a novel metric\nlearning method to evaluate distance between graphs that leverages the power of\nconvolutional neural networks, while exploiting concepts from spectral graph\ntheory to allow these operations on irregular graphs. We demonstrate the\npotential of our method in the field of connectomics, where neuronal pathways\nor functional connections between brain regions are commonly modelled as\ngraphs. In this problem, the definition of an appropriate graph similarity\nfunction is critical to unveil patterns of disruptions associated with certain\nbrain disorders. Experimental results on the ABIDE dataset show that our method\ncan learn a graph similarity metric tailored for a clinical application,\nimproving the performance of a simple k-nn classifier by 11.9% compared to a\ntraditional distance metric.","url_abs":"http://arxiv.org/abs/1703.02161v2","url_pdf":"http://arxiv.org/pdf/1703.02161v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"distance-metric-learning-using-graph","repo_url":"https://github.com/sk1712/gcn_metric_learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"distance-metric-learning-using-graph","repo_url":"https://github.com/sheryl-ai/MVGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"distance-metric-learning-using-graph","repo_url":"https://github.com/sheryl-ai/MemGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"graph-similarity","task_name":"Graph Similarity"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[{"method_slug":"k-nn","method_name":"k-NN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.02161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.02161"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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