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GPNNs alternate\nbetween locally propagating information between nodes in small subgraphs and\nglobally propagating information between the subgraphs. To efficiently\npartition graphs, we experiment with several partitioning algorithms and also\npropose a novel variant for fast processing of large scale graphs. We\nextensively test our model on a variety of semi-supervised node classification\ntasks. Experimental results indicate that GPNNs are either superior or\ncomparable to state-of-the-art methods on a wide variety of datasets for\ngraph-based semi-supervised classification. We also show that GPNNs can achieve\nsimilar performance as standard GNNs with fewer propagation steps.","url_abs":"http://arxiv.org/abs/1803.06272v1","url_pdf":"http://arxiv.org/pdf/1803.06272v1.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":"graph-partition-neural-networks-for-semi","repo_url":"https://github.com/Microsoft/graph-partition-neural-network-samples","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.06272","atlas_url":"https://app.syntology.ai/?focus=1803.06272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.06272"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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