{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T01:41:00Z","timestamp":1773970860914,"version":"3.50.1"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018996","name":"Hellenic Academic Libraries Link","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018996","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005187","name":"University of Athens","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005187","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data & Knowledge Engineering"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.datak.2026.102554","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T16:45:21Z","timestamp":1767804321000},"page":"102554","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["From primes to paths: Enabling fast multi-relational graph analysis"],"prefix":"10.1016","volume":"163","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1910-2758","authenticated-orcid":false,"given":"Konstantinos","family":"Bougiatiotis","sequence":"first","affiliation":[]},{"given":"Georgios","family":"Paliouras","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4\u20135","key":"10.1016\/j.datak.2026.102554_b1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.physrep.2005.10.009","article-title":"Complex networks: Structure and dynamics","volume":"424","author":"Boccaletti","year":"2006","journal-title":"Phys. Rep."},{"key":"10.1016\/j.datak.2026.102554_b2","series-title":"Journal of Physics: Conference Series","article-title":"A survey on application of knowledge graph","volume":"Vol. 1487","author":"Zou","year":"2020"},{"issue":"3","key":"10.1016\/j.datak.2026.102554_b3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/comnet\/cnu016","article-title":"Multilayer networks","volume":"2","author":"Kivel\u00e4","year":"2014","journal-title":"J. Complex Netw."},{"issue":"12","key":"10.1016\/j.datak.2026.102554_b4","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: A survey of approaches and applications","volume":"29","author":"Wang","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"10.1016\/j.datak.2026.102554_b5","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"10.1016\/j.datak.2026.102554_b6","article-title":"A survey on knowledge graphs: Representation, acquisition, and applications","author":"Ji","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.datak.2026.102554_b7","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.datak.2026.102554_b8","series-title":"A survey on the expressive power of graph neural networks","author":"Sato","year":"2020"},{"key":"10.1016\/j.datak.2026.102554_b9","series-title":"Explainable biomedical recommendations via reinforcement learning reasoning on knowledge graphs","author":"Edwards","year":"2021"},{"key":"10.1016\/j.datak.2026.102554_b10","series-title":"IEEE INFOCOM 2014-IEEE Conference on Computer Communications","first-page":"1698","article-title":"Assessment of multi-hop interpersonal trust in social networks by three-valued subjective logic","author":"Liu","year":"2014"},{"key":"10.1016\/j.datak.2026.102554_b11","series-title":"2023 IEEE 36th International Symposium on Computer-Based Medical Systems","first-page":"628","article-title":"Analysing biomedical knowledge graphs using prime adjacency matrices","author":"Bougiatiotis","year":"2023"},{"key":"10.1016\/j.datak.2026.102554_b12","series-title":"Python-graphblas\/python-graphblas: 2024.2.0","author":"Welch","year":"2024"},{"key":"10.1016\/j.datak.2026.102554_b13","series-title":"International Conference on Complex Networks and their Applications","first-page":"75","article-title":"Efficient complex network representation using prime numbers","author":"Bougiatiotis","year":"2023"},{"key":"10.1016\/j.datak.2026.102554_b14","doi-asserted-by":"crossref","first-page":"880","DOI":"10.3389\/fphar.2017.00880","article-title":"ToxiM: a toxicity prediction tool for small molecules developed using machine learning and chemoinformatics approaches","volume":"8","author":"Sharma","year":"2017","journal-title":"Front. Pharmacol."},{"issue":"4","key":"10.1016\/j.datak.2026.102554_b15","doi-asserted-by":"crossref","DOI":"10.1063\/1.4979282","article-title":"Multilayer motif analysis of brain networks","volume":"27","author":"Battiston","year":"2017","journal-title":"Chaos"},{"issue":"2","key":"10.1016\/j.datak.2026.102554_b16","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.1500445","article-title":"Lost in transportation: Information measures and cognitive limits in multilayer navigation","volume":"2","author":"Gallotti","year":"2016","journal-title":"Sci. Adv."},{"issue":"1","key":"10.1016\/j.datak.2026.102554_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2014.07.001","article-title":"The structure and dynamics of multilayer networks","volume":"544","author":"Boccaletti","year":"2014","journal-title":"Phys. Rep."},{"issue":"1","key":"10.1016\/j.datak.2026.102554_b18","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: an interdisciplinary review","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.datak.2026.102554_b19","series-title":"The Semantic Web\u2013ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17\u201321, 2016, Proceedings, Part II 15","first-page":"186","article-title":"A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web","author":"Ristoski","year":"2016"},{"key":"10.1016\/j.datak.2026.102554_b20","series-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull","year":"2017"},{"issue":"2","key":"10.1016\/j.datak.2026.102554_b21","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1007\/s10618-021-00806-z","article-title":"INK: knowledge graph embeddings for node classification","volume":"36","author":"Steenwinckel","year":"2022","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.datak.2026.102554_b22","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"982","article-title":"Graph machine learning through the lens of bilevel optimization","author":"Zheng","year":"2024"},{"issue":"2","key":"10.1016\/j.datak.2026.102554_b23","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s13042-024-02305-1","article-title":"RHGNN: imposing relational inductive bias for heterogeneous graph neural network","volume":"16","author":"Zhu","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.datak.2026.102554_b24","series-title":"Markov process-based graph convolutional networks for entity classification in knowledge graphs","author":"M\u00e4kelburg","year":"2024"},{"key":"10.1016\/j.datak.2026.102554_b25","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","first-page":"1697","article-title":"Relational message passing for knowledge graph completion","author":"Wang","year":"2021"},{"key":"10.1016\/j.datak.2026.102554_b26","series-title":"Deeppath: A reinforcement learning method for knowledge graph reasoning","author":"Xiong","year":"2017"},{"key":"10.1016\/j.datak.2026.102554_b27","doi-asserted-by":"crossref","unstructured":"A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka, T.M. Mitchell, Toward an architecture for never-ending language learning, in: Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010.","DOI":"10.1609\/aaai.v24i1.7519"},{"key":"10.1016\/j.datak.2026.102554_b28","doi-asserted-by":"crossref","unstructured":"T. Dettmers, P. Minervini, P. Stenetorp, S. Riedel, Convolutional 2d knowledge graph embeddings, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 2018.","DOI":"10.1609\/aaai.v32i1.11573"},{"issue":"43","key":"10.1016\/j.datak.2026.102554_b29","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.datak.2026.102554_b30","first-page":"22326","article-title":"Long range graph benchmark","volume":"35","author":"Dwivedi","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.datak.2026.102554_b31","series-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"10.1016\/j.datak.2026.102554_b32","series-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"10.1016\/j.datak.2026.102554_b33","series-title":"A generalization of transformer networks to graphs","author":"Dwivedi","year":"2020"},{"key":"10.1016\/j.datak.2026.102554_b34","article-title":"Rewiring with positional encodings for graph neural networks","author":"Gabrielsson","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"10.1016\/j.datak.2026.102554_b35","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification","volume":"9","author":"Fan","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.datak.2026.102554_b36","series-title":"International Conference on Machine Learning","first-page":"12724","article-title":"A generalization of vit\/mlp-mixer to graphs","author":"He","year":"2023"},{"key":"10.1016\/j.datak.2026.102554_b37","series-title":"CLUE: Chemistry, Life, the Universe and Everything","author":"Cooper","year":"2020"},{"key":"10.1016\/j.datak.2026.102554_b38","unstructured":"N. Lao, T. Mitchell, W. Cohen, Random walk inference and learning in a large scale knowledge base, in: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 2011, pp. 529\u2013539."},{"key":"10.1016\/j.datak.2026.102554_b39","series-title":"Compositional vector space models for knowledge base completion","author":"Neelakantan","year":"2015"},{"key":"10.1016\/j.datak.2026.102554_b40","series-title":"Chains of reasoning over entities, relations, and text using recurrent neural networks","author":"Das","year":"2016"},{"key":"10.1016\/j.datak.2026.102554_b41","doi-asserted-by":"crossref","unstructured":"K. Toutanova, X.V. Lin, W.-t. Yih, H. Poon, C. Quirk, Compositional learning of embeddings for relation paths in knowledge base and text, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, pp. 1434\u20131444.","DOI":"10.18653\/v1\/P16-1136"},{"key":"10.1016\/j.datak.2026.102554_b42","doi-asserted-by":"crossref","unstructured":"Y. Zhang, Q. Yao, Knowledge graph reasoning with relational digraph, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 912\u2013924.","DOI":"10.1145\/3485447.3512008"},{"key":"10.1016\/j.datak.2026.102554_b43","series-title":"International Conference on Machine Learning","first-page":"24737","article-title":"Path neural networks: Expressive and accurate graph neural networks","author":"Michel","year":"2023"},{"key":"10.1016\/j.datak.2026.102554_b44","article-title":"A* net: A scalable path-based reasoning approach for knowledge graphs","volume":"36","author":"Zhu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.datak.2026.102554_b45","series-title":"Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning","author":"Das","year":"2017"},{"key":"10.1016\/j.datak.2026.102554_b46","doi-asserted-by":"crossref","unstructured":"L.A. Gal\u00e1rraga, C. Teflioudi, K. Hose, F. Suchanek, AMIE: association rule mining under incomplete evidence in ontological knowledge bases, in: Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 413\u2013422.","DOI":"10.1145\/2488388.2488425"},{"issue":"1","key":"10.1016\/j.datak.2026.102554_b47","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s00778-023-00800-5","article-title":"Anytime bottom-up rule learning for large-scale knowledge graph completion","volume":"33","author":"Meilicke","year":"2024","journal-title":"VLDB J."},{"key":"10.1016\/j.datak.2026.102554_b48","doi-asserted-by":"crossref","unstructured":"G. Pirr\u00f2, Relatedness and tbox-driven rule learning in large knowledge bases, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 2975\u20132982.","DOI":"10.1609\/aaai.v34i03.5690"},{"key":"10.1016\/j.datak.2026.102554_b49","series-title":"Rnnlogic: Learning logic rules for reasoning on knowledge graphs","author":"Qu","year":"2020"},{"key":"10.1016\/j.datak.2026.102554_b50","doi-asserted-by":"crossref","unstructured":"K. Cheng, J. Liu, W. Wang, Y. Sun, Rlogic: Recursive logical rule learning from knowledge graphs, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 179\u2013189.","DOI":"10.1145\/3534678.3539421"},{"key":"10.1016\/j.datak.2026.102554_b51","series-title":"R5: Rule discovery with reinforced and recurrent relational reasoning","author":"Lu","year":"2022"},{"key":"10.1016\/j.datak.2026.102554_b52","series-title":"Tensorlog: A differentiable deductive database","author":"Cohen","year":"2016"},{"key":"10.1016\/j.datak.2026.102554_b53","article-title":"Differentiable learning of logical rules for knowledge base reasoning","volume":"30","author":"Yang","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.datak.2026.102554_b54","article-title":"Drum: End-to-end differentiable rule mining on knowledge graphs","volume":"32","author":"Sadeghian","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.datak.2026.102554_b55","doi-asserted-by":"crossref","unstructured":"J. Guo, L. Du, W. Bi, Q. Fu, X. Ma, X. Chen, S. Han, D. Zhang, Y. Zhang, Homophily-oriented heterogeneous graph rewiring, in: Proceedings of the ACM Web Conference 2023, 2023, pp. 511\u2013522.","DOI":"10.1145\/3543507.3583454"},{"key":"10.1016\/j.datak.2026.102554_b56","article-title":"Spectral graph theory","volume":"18","author":"Spielman","year":"2012","journal-title":"Comb. Sci. Comput."},{"key":"10.1016\/j.datak.2026.102554_b57","series-title":"Topological Graph Theory","author":"Gross","year":"2001"}],"container-title":["Data & Knowledge Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000017?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000017?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:11:48Z","timestamp":1773965508000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169023X26000017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":57,"alternative-id":["S0169023X26000017"],"URL":"https:\/\/doi.org\/10.1016\/j.datak.2026.102554","relation":{},"ISSN":["0169-023X"],"issn-type":[{"value":"0169-023X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From primes to paths: Enabling fast multi-relational graph analysis","name":"articletitle","label":"Article Title"},{"value":"Data & Knowledge Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.datak.2026.102554","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102554"}}