{"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/evaluation-metrics-for-graph-generative","title":"Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions","arxiv_id":"2106.01098","date":"2021-06-02","proceeding":"ICLR 2022 4","authors":["Leslie O'Bray","Max Horn","Bastian Rieck","Karsten Borgwardt"],"abstract":"Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for such a comparison metric and provide an overview of the status quo of graph generative model comparison in use today, which predominantly relies on the maximum mean discrepancy (MMD). We perform a systematic evaluation of MMD in the context of graph generative model comparison, highlighting some of the challenges and pitfalls researchers inadvertently may encounter. After conducting a thorough analysis of the behaviour of MMD on synthetically-generated perturbed graphs as well as on recently-proposed graph generative models, we are able to provide a suitable procedure to mitigate these challenges and pitfalls. We aggregate our findings into a list of practical recommendations for researchers to use when evaluating graph generative models.","url_abs":"https://arxiv.org/abs/2106.01098v3","url_pdf":"https://arxiv.org/pdf/2106.01098v3.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":"evaluation-metrics-for-graph-generative","repo_url":"https://github.com/borgwardtlab/ggme","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"evaluation-metrics-for-graph-generative","repo_url":"https://github.com/aidos-lab/CFGGME","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.01098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01098"}},"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/borgwardtlab/ggme","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/aidos-lab/CFGGME","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"unverified":15},"by_repo_kind":{"official":{"samples":15,"ran":0,"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":"6c1cf18a77ad5c33","entry":"adj_to_networkx","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"6c1cf18a77ad5c33"}},{"code_sha256_prefix":"e69b3f5ed74f8a34","entry":"clustering_coefficient","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/descriptor_functions.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/descriptor_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"e69b3f5ed74f8a34"}},{"code_sha256_prefix":"09d321100886c8b5","entry":"compute_correlation","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/correlation.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/correlation.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"09d321100886c8b5"}},{"code_sha256_prefix":"5a0fbb727abb2bb5","entry":"degree_distribution","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/descriptor_functions.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/descriptor_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"5a0fbb727abb2bb5"}},{"code_sha256_prefix":"923f7e15bd130842","entry":"ensure_padded","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/utils.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"923f7e15bd130842"}},{"code_sha256_prefix":"31a99a406af1fcf6","entry":"gaussian_kernel","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/kernels.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/kernels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"31a99a406af1fcf6"}},{"code_sha256_prefix":"3996e1aecc59e905","entry":"gaussian_tv","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/kernels.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/kernels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3996e1aecc59e905"}},{"code_sha256_prefix":"72eed74671718b01","entry":"get_open_fn","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"72eed74671718b01"}},{"code_sha256_prefix":"637718871ee800c0","entry":"laplacian_total_variation_kernel","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/kernels.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/kernels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"637718871ee800c0"}},{"code_sha256_prefix":"7253629098c52c80","entry":"load_graphs","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"7253629098c52c80"}},{"code_sha256_prefix":"b8ddf9e892aa4b13","entry":"mmd","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/mmd.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/mmd.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"b8ddf9e892aa4b13"}},{"code_sha256_prefix":"fcd59998ef67f1a7","entry":"mmd_linear_approximation","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/mmd.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/mmd.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"fcd59998ef67f1a7"}},{"code_sha256_prefix":"17653f52e7cb710a","entry":"mmd_variance_estimate","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/mmd.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/mmd.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"17653f52e7cb710a"}},{"code_sha256_prefix":"a401d35796446a06","entry":"normalised_laplacian_spectrum","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/descriptor_functions.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/descriptor_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"a401d35796446a06"}},{"code_sha256_prefix":"323380a27091cb1b","entry":"pad_to_length","repo":"borgwardtlab/ggme","repo_kind":"official","path":"src/metrics/utils.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"323380a27091cb1b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}