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While CNNs demonstrate state-of-the-art performance in graph\nclassification task, such methods are supervised and therefore steer away from\nthe original problem of network representation in task-agnostic manner. Here,\nwe coherently propose an approach for embedding entire graphs and show that our\nfeature representations with SVM classifier increase classification accuracy of\nCNN algorithms and traditional graph kernels. For this we describe a recently\ndiscovered graph object, anonymous walk, on which we design task-independent\nalgorithms for learning graph representations in explicit and distributed way.\nOverall, our work represents a new scalable unsupervised learning of\nstate-of-the-art representations of entire graphs.","url_abs":"http://arxiv.org/abs/1805.11921v3","url_pdf":"http://arxiv.org/pdf/1805.11921v3.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":"anonymous-walk-embeddings","repo_url":"https://github.com/nd7141/AWE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"anonymous-walk-embeddings","repo_url":"https://github.com/paulmorio/geo2dr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-dd","task":"Graph Classification","dataset":"D&D","model":"AWE","rank_in_archive_order":52,"of":53,"metrics":{"Accuracy":"71.51%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-imdb-b","task":"Graph Classification","dataset":"IMDb-B","model":"AWE","rank_in_archive_order":27,"of":51,"metrics":{"Accuracy":"74.45%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-mutag","task":"Graph Classification","dataset":"MUTAG","model":"AWE","rank_in_archive_order":43,"of":74,"metrics":{"Accuracy":"87.87%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.11921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.11921"}},"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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