{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-graph-translation","title":"Deep Graph Translation","arxiv_id":"1805.09980","date":"2018-05-25","proceeding":null,"authors":["Xiaojie Guo","Lingfei Wu","Liang Zhao"],"abstract":"Inspired by the tremendous success of deep generative models on generating\ncontinuous data like image and audio, in the most recent year, few deep graph\ngenerative models have been proposed to generate discrete data such as graphs.\nThey are typically unconditioned generative models which has no control on\nmodes of the graphs being generated. Differently, in this paper, we are\ninterested in a new problem named \\emph{Deep Graph Translation}: given an input\ngraph, we want to infer a target graph based on their underlying (both global\nand local) translation mapping. Graph translation could be highly desirable in\nmany applications such as disaster management and rare event forecasting, where\nthe rare and abnormal graph patterns (e.g., traffic congestions and terrorism\nevents) will be inferred prior to their occurrence even without historical data\non the abnormal patterns for this graph (e.g., a road network or human contact\nnetwork). To achieve this, we propose a novel Graph-Translation-Generative\nAdversarial Networks (GT-GAN) which will generate a graph translator from input\nto target graphs. GT-GAN consists of a graph translator where we propose new\ngraph convolution and deconvolution layers to learn the global and local\ntranslation mapping. A new conditional graph discriminator has also been\nproposed to classify target graphs by conditioning on input graphs. Extensive\nexperiments on multiple synthetic and real-world datasets demonstrate the\neffectiveness and scalability of the proposed GT-GAN.","url_abs":"http://arxiv.org/abs/1805.09980v2","url_pdf":"http://arxiv.org/pdf/1805.09980v2.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":"deep-graph-translation","repo_url":"https://github.com/basiralab/ABMT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-graph-translation","repo_url":"https://github.com/basiralab/CGTS-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.09980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09980"}},"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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