{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:08:31Z","timestamp":1765544911216,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031360268"},{"type":"electronic","value":"9783031360275"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36027-5_47","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T08:02:52Z","timestamp":1688025772000},"page":"597-611","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Strengthening Structural Baselines for\u00a0Graph Classification Using Local Topological Profile"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4336-4288","authenticated-orcid":false,"given":"Jakub","family":"Adamczyk","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1903-8098","authenticated-orcid":false,"given":"Wojciech","family":"Czech","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"issue":"8","key":"47_CR1","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/S0167-8655(97)00060-3","volume":"18","author":"H Bunke","year":"1997","unstructured":"Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(8), 689\u2013694 (1997)","journal-title":"Pattern Recogn. Lett."},{"key":"47_CR2","unstructured":"Cai, C., Wang, Y.: A simple yet effective baseline for non-attributed graph classification. arXiv preprint arXiv:1811.03508 (2018)"},{"issue":"15","key":"47_CR3","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1016\/j.patrec.2012.03.024","volume":"33","author":"W Czech","year":"2012","unstructured":"Czech, W.: Invariants of distance k-graphs for graph embedding. Pattern Recogn. Lett. 33(15), 1968\u20131979 (2012)","journal-title":"Pattern Recogn. Lett."},{"key":"47_CR4","unstructured":"Errica, F., Podda, M., Bacciu, D., Micheli, A.: A fair comparison of graph neural networks for graph classification. arXiv preprint arXiv:1912.09893 (2019)"},{"key":"47_CR5","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich, H., Wegner, J.K., Sieker, F., Zell, A.: Optimal assignment kernels for attributed molecular graphs. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 225\u2013232 (2005)","DOI":"10.1145\/1102351.1102380"},{"issue":"12","key":"47_CR7","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1073\/pnas.122653799","volume":"99","author":"M Girvan","year":"2002","unstructured":"Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821\u20137826 (2002)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"47_CR8","unstructured":"Hagberg, A., Swart, P., S Chult, D.: Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab. (LANL), Los Alamos, NM (United States) (2008)"},{"key":"47_CR9","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"47_CR10","unstructured":"Haussler, D., et al.: Convolution kernels on discrete structures. Technical report, Citeseer (1999)"},{"key":"47_CR11","first-page":"22118","volume":"33","author":"W Hu","year":"2020","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. Adv. Neural Inf. Process. Syst. 33, 22118\u201322133 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"47_CR12","doi-asserted-by":"crossref","unstructured":"Jaccard, P.: The distribution of the flora in the alpine zone. 1. New Phytol. 11(2), 37\u201350 (1912)","DOI":"10.1111\/j.1469-8137.1912.tb05611.x"},{"issue":"1","key":"47_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0195-3","volume":"5","author":"NM Kriege","year":"2020","unstructured":"Kriege, N.M., Johansson, F.D., Morris, C.: A survey on graph kernels. Appl. Netw. Sci. 5(1), 1\u201342 (2020)","journal-title":"Appl. Netw. Sci."},{"issue":"9","key":"47_CR14","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/TKDE.2004.33","volume":"16","author":"M Kuramochi","year":"2004","unstructured":"Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent subgraphs. IEEE Trans. Knowl. Data Eng. 16(9), 1038\u20131051 (2004)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"47_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/978-3-030-77964-1_18","volume-title":"Computational Science \u2013 ICCS 2021","author":"R \u0141azarz","year":"2021","unstructured":"\u0141azarz, R., Idzik, M.: Relation order histograms as a network embedding tool. In: Paszynski, M., Kranzlm\u00fcller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2021. LNCS, vol. 12743, pp. 224\u2013237. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77964-1_18"},{"key":"47_CR16","unstructured":"Li, Y., Gu, C., Dullien, T., Vinyals, O., Kohli, P.: Graph matching networks for learning the similarity of graph structured objects. In: International Conference on Machine Learning, pp. 3835\u20133845. PMLR (2019)"},{"key":"47_CR17","doi-asserted-by":"crossref","unstructured":"Lindner, G., Staudt, C.L., Hamann, M., Meyerhenke, H., Wagner, D.: Structure-preserving sparsification of social networks. In: Proceedings of the 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 448\u2013454 (2015)","DOI":"10.1145\/2808797.2809313"},{"key":"47_CR18","unstructured":"Liu, R., et al.: Taxonomy of benchmarks in graph representation learning. arXiv preprint arXiv:2206.07729 (2022)"},{"issue":"1","key":"47_CR19","first-page":"1934","volume":"20","author":"P Probst","year":"2019","unstructured":"Probst, P., Boulesteix, A.L., Bischl, B.: Tunability: importance of hyperparameters of machine learning algorithms. J. Mach. Learn. Res. 20(1), 1934\u20131965 (2019)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"47_CR20","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1301","volume":"9","author":"P Probst","year":"2019","unstructured":"Probst, P., Wright, M.N., Boulesteix, A.L.: Hyperparameters and tuning strategies for random forest. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(3), e1301 (2019)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"47_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-540-89689-0_33","volume-title":"Structural, Syntactic, and Statistical Pattern Recognition","author":"K Riesen","year":"2008","unstructured":"Riesen, K., Bunke, H.: IAM graph database repository for graph based pattern recognition and machine learning. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) SSPR \/SPR 2008. LNCS, vol. 5342, pp. 287\u2013297. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-89689-0_33"},{"issue":"5","key":"47_CR22","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742\u2013754 (2010)","journal-title":"J. Chem. Inf. Model."},{"key":"47_CR23","unstructured":"Shervashidze, N., Schweitzer, P., Van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-lehman graph kernels. J. Mach. Learn. Res. 12(9) (2011)"},{"key":"47_CR24","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3693\u20133702 (2017)","DOI":"10.1109\/CVPR.2017.11"},{"issue":"4","key":"47_CR25","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1017\/nws.2016.20","volume":"4","author":"CL Staudt","year":"2016","unstructured":"Staudt, C.L., Sazonovs, A., Meyerhenke, H.: NetworKit: a tool suite for large-scale complex network analysis. Netw. Sci. 4(4), 508\u2013530 (2016)","journal-title":"Netw. Sci."},{"issue":"7","key":"47_CR26","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1109\/TPAMI.2005.145","volume":"27","author":"RC Wilson","year":"2005","unstructured":"Wilson, R.C., Hancock, E.R., Luo, B.: Pattern vectors from algebraic graph theory. IEEE Trans. Pattern Anal. Mach. Intell. 27(7), 1112\u20131124 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"47_CR27","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"47_CR28","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"key":"47_CR29","doi-asserted-by":"crossref","unstructured":"Yanardag, P., Vishwanathan, S.: Deep graph kernels. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1365\u20131374 (2015)","DOI":"10.1145\/2783258.2783417"},{"key":"47_CR30","unstructured":"Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"47_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"47_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.690049","volume":"12","author":"XM Zhang","year":"2021","unstructured":"Zhang, X.M., Liang, L., Liu, L., Tang, M.J.: Graph neural networks and their current applications in bioinformatics. Front. Genet. 12, 690049 (2021)","journal-title":"Front. Genet."},{"issue":"4","key":"47_CR33","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.104.044315","volume":"104","author":"YJ Zhang","year":"2021","unstructured":"Zhang, Y.J., Yang, K.C., Radicchi, F.: Systematic comparison of graph embedding methods in practical tasks. Phys. Rev. E 104(4), 044315 (2021)","journal-title":"Phys. Rev. E"},{"issue":"1","key":"47_CR34","first-page":"1","volume":"13","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Zheng, H., Huang, X., Hao, S., Li, D., Zhao, J.: Graph neural networks: taxonomy, advances, and trends. ACM Trans. Intell. Syst. Technol. (TIST) 13(1), 1\u201354 (2022)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36027-5_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T08:08:15Z","timestamp":1688026095000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36027-5_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360268","9783031360275"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36027-5_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"188","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}