{"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/sega-structural-entropy-guided-anchor-view","title":"SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning","arxiv_id":"2305.04501","date":"2023-05-08","proceeding":null,"authors":["Junran Wu","Xueyuan Chen","Bowen Shi","Shangzhe Li","Ke Xu"],"abstract":"In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contrastive learning methods generally produce views via random corruption or learning, which could lead to the loss of essential information and alteration of semantic information. An anchor view that maintains the essential information of input graphs for contrastive learning has been hardly investigated. In this paper, based on the theory of graph information bottleneck, we deduce the definition of this anchor view; put differently, \\textit{the anchor view with essential information of input graph is supposed to have the minimal structural uncertainty}. Furthermore, guided by structural entropy, we implement the anchor view, termed \\textbf{SEGA}, for graph contrastive learning. We extensively validate the proposed anchor view on various benchmarks regarding graph classification under unsupervised, semi-supervised, and transfer learning and achieve significant performance boosts compared to the state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2305.04501v2","url_pdf":"https://arxiv.org/pdf/2305.04501v2.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":"sega-structural-entropy-guided-anchor-view","repo_url":"https://github.com/wu-junran/sega","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.04501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04501"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wu-junran/sega","reach":null}],"summary":{"ran_honours":2,"ran":1,"ran_draft_wrong":3,"ran_fixture":2,"unverified":1},"by_repo_kind":{"official":{"samples":9,"ran":8,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"de84476db7dd33cf","entry":"CombineDelta","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/codingTree.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/codingTree.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"de84476db7dd33cf"}},{"code_sha256_prefix":"8335fd74dc310937","entry":"CompressDelta","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/codingTree.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/codingTree.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8335fd74dc310937"}},{"code_sha256_prefix":"8811a40392658c9f","entry":"PartitionTreeV2","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/codingTree.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/codingTree.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8811a40392658c9f"}},{"code_sha256_prefix":"33da216971e030f9","entry":"drop_nodes","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/aug.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/aug.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"33da216971e030f9"}},{"code_sha256_prefix":"5cc1437e6af51bc4","entry":"permute_edges","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/aug.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/aug.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"5cc1437e6af51bc4"}},{"code_sha256_prefix":"589e7c44131e0c22","entry":"randomforest_classify","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/evaluate_embedding.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/evaluate_embedding.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"589e7c44131e0c22"}},{"code_sha256_prefix":"82656aebda7aec5b","entry":"subgraph","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/aug.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/aug.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"82656aebda7aec5b"}},{"code_sha256_prefix":"4f29b7d00e11ae45","entry":"svc_classify","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/evaluate_embedding.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/evaluate_embedding.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"4f29b7d00e11ae45"}},{"code_sha256_prefix":"6b9fd20aae72d753","entry":"logistic_classify","repo":"wu-junran/sega","repo_kind":"official","path":"unsupervised_TU/evaluate_embedding.py","file_url":"https://github.com/wu-junran/sega/blob/HEAD/unsupervised_TU/evaluate_embedding.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6b9fd20aae72d753"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}