{"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/motif-aware-riemannian-graph-neural-network","title":"Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning","arxiv_id":"2401.01232","date":"2024-01-02","proceeding":null,"authors":["Li Sun","Zhenhao Huang","Zixi Wang","Feiyang Wang","Hao Peng","Philip Yu"],"abstract":"Graphs are typical non-Euclidean data of complex structures. In recent years, Riemannian graph representation learning has emerged as an exciting alternative to Euclidean ones. However, Riemannian methods are still in an early stage: most of them present a single curvature (radius) regardless of structural complexity, suffer from numerical instability due to the exponential/logarithmic map, and lack the ability to capture motif regularity. In light of the issues above, we propose the problem of \\emph{Motif-aware Riemannian Graph Representation Learning}, seeking a numerically stable encoder to capture motif regularity in a diverse-curvature manifold without labels. To this end, we present a novel Motif-aware Riemannian model with Generative-Contrastive learning (MotifRGC), which conducts a minmax game in Riemannian manifold in a self-supervised manner. First, we propose a new type of Riemannian GCN (D-GCN), in which we construct a diverse-curvature manifold by a product layer with the diversified factor, and replace the exponential/logarithmic map by a stable kernel layer. Second, we introduce a motif-aware Riemannian generative-contrastive learning to capture motif regularity in the constructed manifold and learn motif-aware node representation without external labels. Empirical results show the superiority of MofitRGC.","url_abs":"https://arxiv.org/abs/2401.01232v1","url_pdf":"https://arxiv.org/pdf/2401.01232v1.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":"motif-aware-riemannian-graph-neural-network","repo_url":"https://github.com/riemanngraph/motifrgc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2401.01232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.01232"}},"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/riemanngraph/motifrgc","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":0,"samples":[{"code_sha256_prefix":"0d4dfceee7018e3d","entry":"FermiDiracDecoder","repo":"riemanngraph/motifrgc","repo_kind":"official","path":"models.py","file_url":"https://github.com/riemanngraph/motifrgc/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0d4dfceee7018e3d"}},{"code_sha256_prefix":"cbb8804ff702cf89","entry":"GAT","repo":"riemanngraph/motifrgc","repo_kind":"official","path":"models.py","file_url":"https://github.com/riemanngraph/motifrgc/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cbb8804ff702cf89"}},{"code_sha256_prefix":"edd81c40e27d23fc","entry":"GCN","repo":"riemanngraph/motifrgc","repo_kind":"official","path":"models.py","file_url":"https://github.com/riemanngraph/motifrgc/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"edd81c40e27d23fc"}},{"code_sha256_prefix":"f419f73bc3b2cfe8","entry":"GraphSAGE","repo":"riemanngraph/motifrgc","repo_kind":"official","path":"models.py","file_url":"https://github.com/riemanngraph/motifrgc/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f419f73bc3b2cfe8"}},{"code_sha256_prefix":"c426aa4122c719cd","entry":"Model","repo":"riemanngraph/motifrgc","repo_kind":"official","path":"models.py","file_url":"https://github.com/riemanngraph/motifrgc/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c426aa4122c719cd"}},{"code_sha256_prefix":"e88ac555d2d0018a","entry":"RiemannianFeatures","repo":"riemanngraph/motifrgc","repo_kind":"official","path":"models.py","file_url":"https://github.com/riemanngraph/motifrgc/blob/HEAD/models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e88ac555d2d0018a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}