{"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/topology-aware-correlations-between-relations","title":"Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs","arxiv_id":"2103.03642","date":"2021-03-05","proceeding":null,"authors":["Jiajun Chen","Huarui He","Feng Wu","Jie Wang"],"abstract":"Inductive link prediction -- where entities during training and inference stages can be different -- has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs. To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs. Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction. Experiments demonstrate that TACT can effectively model semantic correlations between relations, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the inductive link prediction task.","url_abs":"https://arxiv.org/abs/2103.03642v1","url_pdf":"https://arxiv.org/pdf/2103.03642v1.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":"topology-aware-correlations-between-relations","repo_url":"https://github.com/MIRALab-USTC/KG-TACT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"inductive-link-prediction","task_name":"Inductive Link Prediction"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"world-knowledge","task_name":"World Knowledge"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.03642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.03642"}},"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/MIRALab-USTC/KG-TACT","reach":null}],"summary":{"ran":4,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"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":6,"samples":[{"code_sha256_prefix":"b72691639fe6422f","entry":"Aggregator","repo":"MIRALab-USTC/KG-TACT","repo_kind":"official","path":"code/Ranking/model/dgl/rgcn_model.py","file_url":"https://github.com/MIRALab-USTC/KG-TACT/blob/HEAD/code/Ranking/model/dgl/rgcn_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b72691639fe6422f"}},{"code_sha256_prefix":"0b9c32ac6686b508","entry":"GRUAggregator","repo":"MIRALab-USTC/KG-TACT","repo_kind":"official","path":"code/Ranking/model/dgl/rgcn_model.py","file_url":"https://github.com/MIRALab-USTC/KG-TACT/blob/HEAD/code/Ranking/model/dgl/rgcn_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0b9c32ac6686b508"}},{"code_sha256_prefix":"13710970cf0a5c04","entry":"MLPAggregator","repo":"MIRALab-USTC/KG-TACT","repo_kind":"official","path":"code/Ranking/model/dgl/rgcn_model.py","file_url":"https://github.com/MIRALab-USTC/KG-TACT/blob/HEAD/code/Ranking/model/dgl/rgcn_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"13710970cf0a5c04"}},{"code_sha256_prefix":"53b01e6ba45b612b","entry":"SumAggregator","repo":"MIRALab-USTC/KG-TACT","repo_kind":"official","path":"code/Ranking/model/dgl/rgcn_model.py","file_url":"https://github.com/MIRALab-USTC/KG-TACT/blob/HEAD/code/Ranking/model/dgl/rgcn_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"53b01e6ba45b612b"}},{"code_sha256_prefix":"501363f5d25987be","entry":"RGCN","repo":"MIRALab-USTC/KG-TACT","repo_kind":"official","path":"code/Ranking/model/dgl/rgcn_model.py","file_url":"https://github.com/MIRALab-USTC/KG-TACT/blob/HEAD/code/Ranking/model/dgl/rgcn_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"501363f5d25987be"}},{"code_sha256_prefix":"bb295f8a7aded734","entry":"RGCNLayer","repo":"MIRALab-USTC/KG-TACT","repo_kind":"official","path":"code/Ranking/model/dgl/rgcn_model.py","file_url":"https://github.com/MIRALab-USTC/KG-TACT/blob/HEAD/code/Ranking/model/dgl/rgcn_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bb295f8a7aded734"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}