{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:41:23Z","timestamp":1742978483947,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030337773"},{"type":"electronic","value":"9783030337780"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33778-0_21","type":"book-chapter","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T14:28:50Z","timestamp":1571408930000},"page":"261-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Symbolic Graph Embedding Using Frequent Pattern Mining"],"prefix":"10.1007","author":[{"given":"Bla\u017e","family":"\u0160krlj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nada","family":"Lavra\u010d","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Kralj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,16]]},"reference":[{"key":"21_CR1","unstructured":"Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487\u2013499 (1994)"},{"issue":"8","key":"21_CR2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR3","unstructured":"Borgelt, C.: Efficient implementations of apriori and eclat. In: FIMI 2003: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (2003)"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Borgelt, C.: An implementation of the FP-growth algorithm. In: Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp. 1\u20135. ACM (2005)","DOI":"10.1145\/1133905.1133907"},{"key":"21_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-319-68288-4_12","volume-title":"The Semantic Web \u2013 ISWC 2017","author":"M Cochez","year":"2017","unstructured":"Cochez, M., Ristoski, P., Ponzetto, S.P., Paulheim, H.: Global RDF vector space embeddings. ISWC 2017. LNCS, vol. 10587, pp. 190\u2013207. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68288-4_12"},{"key":"21_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/978-3-319-99960-9_2","volume-title":"Inductive Logic Programming","author":"T Dash","year":"2018","unstructured":"Dash, T., Srinivasan, A., Vig, L., Orhobor, O.I., King, R.D.: Large-scale assessment of deep relational machines. In: Riguzzi, F., Bellodi, E., Zese, R. (eds.) ILP 2018. LNCS (LNAI), vol. 11105, pp. 22\u201337. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99960-9_2"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135\u2013144. ACM (2017)","DOI":"10.1145\/3097983.3098036"},{"issue":"1","key":"21_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10994-013-5392-1","volume":"94","author":"MV Fran\u00e7a","year":"2014","unstructured":"Fran\u00e7a, M.V., Zaverucha, G., Garcez, A.S.D.: Fast relational learning using bottom clause propositionalization with artificial neural networks. Mach. Learn. 94(1), 81\u2013104 (2014)","journal-title":"Mach. Learn."},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864. ACM (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"21_CR10","unstructured":"Hagberg, A., Swart, P., Chult, D.S.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference (SciPy), January 2008"},{"issue":"1","key":"21_CR11","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s10618-006-0059-1","volume":"15","author":"J Han","year":"2007","unstructured":"Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55\u201386 (2007)","journal-title":"Data Min. Knowl. Discov."},{"key":"21_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/3-540-45372-5_2","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"A Inokuchi","year":"2000","unstructured":"Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., \u017bytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13\u201323. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45372-5_2"},{"key":"21_CR13","unstructured":"Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python (2001). http:\/\/www.scipy.org\/"},{"issue":"1","key":"21_CR14","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10844-017-0444-9","volume":"50","author":"J Kralj","year":"2018","unstructured":"Kralj, J., Robnik-\u0160ikonja, M., Lavra\u010d, N.: HINMINE: heterogeneous information network mining with information retrieval heuristics. J. Intell. Inf. Syst. 50(1), 29\u201361 (2018)","journal-title":"J. Intell. Inf. Syst."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Lam, S.K., Pitrou, A., Seibert, S.: Numba: A LLVM-based python JIT compiler. In: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, p. 7. ACM (2015)","DOI":"10.1145\/2833157.2833162"},{"key":"21_CR16","volume-title":"Inductive Logic Programming: Techniques and Applications","author":"N Lavra\u010d","year":"1994","unstructured":"Lavra\u010d, N., D\u017eeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 631\u2013636. ACM (2006)","DOI":"10.1145\/1150402.1150479"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Maiya, A.S., Berger-Wolf, T.Y.: Sampling community structure. In: Proceedings of the 19th International Conference on World Wide Web, pp. 701\u2013710. ACM (2010)","DOI":"10.1145\/1772690.1772762"},{"issue":"29","key":"21_CR19","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes, L., Healy, J., Saul, N., Grossberger, L.: UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018)","journal-title":"J. Open Source Softw."},{"issue":"Oct","key":"21_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/3-540-44801-2_8","volume-title":"Data Warehousing and Knowledge Discovery","author":"R Perego","year":"2001","unstructured":"Perego, R., Orlando, S., Palmerini, P.: Enhancing the Apriori algorithm for frequent set counting. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 71\u201382. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44801-2_8"},{"issue":"17\u201318","key":"21_CR22","doi-asserted-by":"publisher","first-page":"6442","DOI":"10.1016\/j.eswa.2015.04.017","volume":"42","author":"M Perov\u0161ek","year":"2015","unstructured":"Perov\u0161ek, M., Vavpeti\u010d, A., Kranjc, J., Cestnik, B., Lavra\u010d, N.: Wordification: propositionalization by unfolding relational data into bags of words. Expert Syst. Appl. 42(17\u201318), 6442\u20136456 (2015)","journal-title":"Expert Syst. Appl."},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"21_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1007\/978-3-319-46523-4_30","volume-title":"The Semantic Web \u2013 ISWC 2016","author":"P Ristoski","year":"2016","unstructured":"Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 498\u2013514. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46523-4_30"},{"issue":"2","key":"21_CR25","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2018","unstructured":"Shi, C., Hu, B., Zhao, W.X., Philip, S.Y.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357\u2013370 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR26","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/978-3-030-05411-3_60","volume-title":"Complex Networks and Their Applications VII","author":"B \u0160krlj","year":"2019","unstructured":"\u0160krlj, B., Kralj, J., Lavra\u010d, N.: Py3plex: a library for scalable multilayer network analysis and visualization. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Li\u00f3, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 812, pp. 757\u2013768. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05411-3_60"},{"key":"21_CR27","unstructured":"Srinivasan, A.: The Aleph Manual (2001)"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Mei, Q.: Pte: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165\u20131174. ACM (2015)","DOI":"10.1145\/2783258.2783307"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077. International World Wide Web Conferences Steering Committee (2015)","DOI":"10.1145\/2736277.2741093"},{"issue":"2","key":"21_CR30","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MCSE.2011.37","volume":"13","author":"SVD Walt","year":"2011","unstructured":"Walt, S.V.D., Colbert, S.C., Varoquaux, G.: The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22\u201330 (2011)","journal-title":"Comput. Sci. Eng."},{"issue":"1\u20134","key":"21_CR31","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s13042-010-0001-0","volume":"1","author":"Y Zhang","year":"2010","unstructured":"Zhang, Y., Jin, R., Zhou, Z.H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. Cybern. 1(1\u20134), 43\u201352 (2010)","journal-title":"Int. J. Mach. Learn. Cybern."}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33778-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:33:56Z","timestamp":1709825636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33778-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030337773","9783030337780"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33778-0_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discovery Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Split","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Croatia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dis2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ds2019.irb.hr\/","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":"63","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":"21","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":"19","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":"33% - 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":"3","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":"4","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)"}}]}}