{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T04:21:35Z","timestamp":1747801295061,"version":"3.41.0"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"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":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10115-025-02357-x","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T17:35:47Z","timestamp":1739468147000},"page":"4703-4736","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Navigating complexity: a comprehensive review of heterogeneous information networks and embedding techniques"],"prefix":"10.1007","volume":"67","author":[{"given":"Khouloud","family":"Ammar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wissem","family":"Inoubli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sami","family":"Zghal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Engelbert Mephu","family":"Nguifo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"issue":"3","key":"2357_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961194","volume":"2","author":"F Bonchi","year":"2011","unstructured":"Bonchi F, Castillo C, Gionis A, Jaimes A (2011) Social network analysis and mining for business applications. ACM Trans Intell Syst Technol 2(3):1\u201337. https:\/\/doi.org\/10.1145\/1961189.1961194","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2357_CR2","doi-asserted-by":"publisher","unstructured":"Saidi R, Maddouri M, Mephu Nguifo E (2009) Comparing graph-based representations of protein for mining purposes. In: Proceedings of the ACM SIGKDD Workshop on Statistical and Relational Learning in Bioinformatics, Paris, France, pp 35\u201338. https:\/\/doi.org\/10.1145\/1562090.1562098 . https:\/\/hal.archives-ouvertes.fr\/hal-02134390","DOI":"10.1145\/1562090.1562098"},{"key":"2357_CR3","doi-asserted-by":"publisher","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"2357_CR4","doi-asserted-by":"publisher","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. WWW \u201915, pp 1067\u20131077. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https:\/\/doi.org\/10.1145\/2736277.2741093","DOI":"10.1145\/2736277.2741093"},{"key":"2357_CR5","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks","DOI":"10.1145\/2939672.2939754"},{"key":"2357_CR6","doi-asserted-by":"crossref","unstructured":"Yang C, Zhang J, Wang H, Li S, Kim M, Walker M, Xiao Y, Han J (2020) Relation learning on social networks with multi-modal graph edge variational autoencoders. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp 699\u2013707","DOI":"10.1145\/3336191.3371829"},{"key":"2357_CR7","doi-asserted-by":"publisher","unstructured":"Wei X, Xu L, Cao B, Yu PS (2017) Cross view link prediction by learning noise-resilient representation consensus. In: Proceedings of the 26th International Conference on World Wide Web. WWW \u201917, pp. 1611\u20131619. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https:\/\/doi.org\/10.1145\/3038912.3052575","DOI":"10.1145\/3038912.3052575"},{"key":"2357_CR8","unstructured":"Niepert M, Ahmed M, Kutzkov K (2016) Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp 2014\u20132023. PMLR"},{"key":"2357_CR9","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2015) Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"2357_CR10","doi-asserted-by":"publisher","unstructured":"Ribeiro LFR, Saverese PHP, Figueiredo DR (2017) struc2vec. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https:\/\/doi.org\/10.1145\/3097983.3098061","DOI":"10.1145\/3097983.3098061"},{"key":"2357_CR11","unstructured":"Liaw A (2002) Classification and regression by randomforest. R news"},{"key":"2357_CR12","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"2357_CR13","doi-asserted-by":"crossref","unstructured":"Seo Y, Defferrard M, Vandergheynst P, Bresson X (2018) Structured sequence modeling with graph convolutional recurrent networks. In: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25. Springer, p 362\u2013373","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"2357_CR14","doi-asserted-by":"publisher","unstructured":"He J, Li M, Zhang H-J, Tong H, Zhang C (2004) Manifold-ranking based image retrieval. In: Proceedings of the 12th Annual ACM International Conference on Multimedia. MULTIMEDIA \u201904, pp. 9\u201316. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1027527.1027531","DOI":"10.1145\/1027527.1027531"},{"key":"2357_CR15","doi-asserted-by":"publisher","unstructured":"Xiao Y, Krishnan A, Sundaram H (2020) Discovering strategic behaviors for collaborative content-production in social networks. Proceedings of The Web Conference 2020. https:\/\/doi.org\/10.1145\/3366423.3380274","DOI":"10.1145\/3366423.3380274"},{"key":"2357_CR16","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry"},{"key":"2357_CR17","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26"},{"key":"2357_CR18","doi-asserted-by":"publisher","unstructured":"Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: A neural approach for poi recommendation. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201917, pp 1245\u20131254. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3097983.3098094","DOI":"10.1145\/3097983.3098094"},{"key":"2357_CR19","doi-asserted-by":"publisher","unstructured":"Wei X, Xu L, Cao B, Yu PS (2017) Cross view link prediction by learning noise-resilient representation consensus. In: Proceedings of the 26th International Conference on World Wide Web. WWW \u201917, pp 1611\u20131619. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https:\/\/doi.org\/10.1145\/3038912.3052575","DOI":"10.1145\/3038912.3052575"},{"issue":"1","key":"2357_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","volume":"29","author":"C Shi","year":"2017","unstructured":"Shi C, Li Y, Zhang J, Sun Y, Yu PS (2017) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17\u201337. https:\/\/doi.org\/10.1109\/TKDE.2016.2598561","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2357_CR21","doi-asserted-by":"publisher","unstructured":"Zhang J-D, Chow C-Y (2015) Geosoca: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201915, pp 443\u2013452. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2766462.2767711","DOI":"10.1145\/2766462.2767711"},{"key":"2357_CR22","doi-asserted-by":"crossref","unstructured":"Yang C, Hoang DH, Mikolov T, Han J (2019) Place deduplication with embeddings","DOI":"10.1145\/3308558.3313456"},{"key":"2357_CR23","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/978-3-319-46227-1_38","volume-title":"Machine learning and knowledge discovery in databases","author":"L Dos Santos","year":"2016","unstructured":"Dos Santos L, Piwowarski B, Gallinari P (2016) Multilabel classification on heterogeneous graphs with gaussian embeddings. In: Frasconi P, Landwehr N, Manco G, Vreeken J (eds) Machine learning and knowledge discovery in databases. Springer, Cham, pp 606\u2013622"},{"issue":"5","key":"2357_CR24","doi-asserted-by":"publisher","first-page":"625","DOI":"10.14778\/3055540.3055554","volume":"10","author":"D Eswaran","year":"2017","unstructured":"Eswaran D, G\u00fcnnemann S, Faloutsos C, Makhija D, Kumar M (2017) Zoobp: belief propagation for heterogeneous networks. Proc VLDB Endow 10(5):625\u2013636","journal-title":"Proc VLDB Endow"},{"key":"2357_CR25","doi-asserted-by":"crossref","unstructured":"Chen T, Sun Y (2016) Task-guided and path-augmented heterogeneous network embedding for author identification","DOI":"10.1145\/3018661.3018735"},{"key":"2357_CR26","doi-asserted-by":"crossref","unstructured":"Geng X, Zhang H, Bian J, Chua T-S (2015) Learning image and user features for recommendation in social networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4274\u20134282","DOI":"10.1109\/ICCV.2015.486"},{"key":"2357_CR27","doi-asserted-by":"publisher","unstructured":"Fan S, Zhu J, Han X, Shi C, Hu L, Ma B, Li Y (2019) Metapath-guided heterogeneous graph neural network for intent recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery And; Data Mining. KDD \u201919, pp. 2478\u20132486. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3292500.3330673","DOI":"10.1145\/3292500.3330673"},{"key":"2357_CR28","doi-asserted-by":"publisher","unstructured":"Zhao J, Zhou Z, Guan Z, Zhao W, Ning W, Qiu G, He X (2019) Intentgc. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https:\/\/doi.org\/10.1145\/3292500.3330686","DOI":"10.1145\/3292500.3330686"},{"key":"2357_CR29","doi-asserted-by":"crossref","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1105\u20131114","DOI":"10.1145\/2939672.2939751"},{"issue":"6","key":"2357_CR30","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373\u20131396","journal-title":"Neural Comput"},{"issue":"9","key":"2357_CR31","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2357_CR32","unstructured":"Mikolov T (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"2357_CR33","unstructured":"Yang C, Xiao Y, Zhang Y, Sun Y, Han J (2020) Heterogeneous network representation learning: a unified framework with survey and benchmark. TKDE"},{"key":"2357_CR34","unstructured":"Keele S et al (2007) Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, ver. 2.3 ebse technical report. ebse"},{"key":"2357_CR35","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) 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","DOI":"10.1145\/3097983.3098036"},{"key":"2357_CR36","doi-asserted-by":"crossref","unstructured":"Fu T-y, Lee W-C, Lei Z (2017) Hin2vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1797\u20131806","DOI":"10.1145\/3132847.3132953"},{"key":"2357_CR37","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26"},{"key":"2357_CR38","doi-asserted-by":"crossref","unstructured":"He Y, Song Y, Li J, Ji C, Peng J, Peng H (2019) Hetespaceywalk: a heterogeneous spacey random walk for heterogeneous information network embedding. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp 639\u2013648","DOI":"10.1145\/3357384.3358061"},{"key":"2357_CR39","doi-asserted-by":"publisher","unstructured":"Lee S, Park C, Yu H (2019) Bhin2vec: Balancing the type of relation in heterogeneous information network. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. CIKM \u201919, pp 619\u2013628. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3357384.3357893","DOI":"10.1145\/3357384.3357893"},{"key":"2357_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang Y, Shi C (2019) Hyperbolic heterogeneous information network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 5337\u20135344","DOI":"10.1609\/aaai.v33i01.33015337"},{"key":"2357_CR41","doi-asserted-by":"crossref","unstructured":"Hussein R, Yang D, Cudr\u00e9-Mauroux P (2018) Are meta-paths necessary? Revisiting heterogeneous graph embeddings. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 437\u2013446","DOI":"10.1145\/3269206.3271777"},{"issue":"6","key":"2357_CR42","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1093\/bioinformatics\/btaa906","volume":"37","author":"MA Basher","year":"2021","unstructured":"Basher MA, Rahman A, Hallam SJ (2021) Leveraging heterogeneous network embedding for metabolic pathway prediction. Bioinformatics 37(6):822\u2013829","journal-title":"Bioinformatics"},{"key":"2357_CR43","doi-asserted-by":"crossref","unstructured":"Li Z, Zheng W, Lin X, Zhao Z, Wang Z, Wang Y, Jian X, Chen L, Yan Q, Mao T (2020) Transn: heterogeneous network representation learning by translating node embeddings. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp 589\u2013600. IEEE","DOI":"10.1109\/ICDE48307.2020.00057"},{"issue":"2","key":"2357_CR44","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TIP.2015.2507401","volume":"25","author":"F Wu","year":"2016","unstructured":"Wu F, Lu X, Song J, Yan S, Zhang ZM, Rui Y, Zhuang Y (2016) Learning of multimodal representations with random walks on the click graph. IEEE Trans Image Process 25(2):630\u2013642. https:\/\/doi.org\/10.1109\/TIP.2015.2507401","journal-title":"IEEE Trans Image Process"},{"key":"2357_CR45","first-page":"3082","volume":"18","author":"H Zhang","year":"2018","unstructured":"Zhang H, Qiu L, Yi L, Song Y (2018) Scalable multiplex network embedding. IJCAI 18:3082\u20133088","journal-title":"IJCAI"},{"key":"2357_CR46","unstructured":"Shang J, Qu M, Liu J, Kaplan LM, Han J, Peng J (2016) Meta-path guided embedding for similarity search in large-scale heterogeneous information networks"},{"issue":"11","key":"2357_CR47","doi-asserted-by":"publisher","first-page":"992","DOI":"10.14778\/3402707.3402736","volume":"4","author":"Y Sun","year":"2011","unstructured":"Sun Y, Han J, Yan X, Yu PS, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992\u20131003","journal-title":"Proc VLDB Endow"},{"key":"2357_CR48","doi-asserted-by":"publisher","unstructured":"Chen H, Yin H, Wang W, Wang H, Nguyen QVH, Li X (2018) Pme: Projected metric embedding on heterogeneous networks for link prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201918, pp 1177\u20131186. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3219819.3219986","DOI":"10.1145\/3219819.3219986"},{"key":"2357_CR49","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Mei Q (2015) 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","DOI":"10.1145\/2783258.2783307"},{"issue":"6","key":"2357_CR50","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1037\/h0071325","volume":"24","author":"H Hotelling","year":"1933","unstructured":"Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417","journal-title":"J Educ Psychol"},{"key":"2357_CR51","doi-asserted-by":"publisher","unstructured":"Zhang C, Swami A, Chawla NV (2019) Shne: Representation learning for semantic-associated heterogeneous networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. WSDM \u201919, pp 690\u2013698. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3289600.3291001","DOI":"10.1145\/3289600.3291001"},{"issue":"1","key":"2357_CR52","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 SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2357_CR53","doi-asserted-by":"crossref","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The World Wide Web Conference, pp 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"2357_CR54","doi-asserted-by":"publisher","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. Proceedings of The Web Conference 2020. https:\/\/doi.org\/10.1145\/3366423.3380297","DOI":"10.1145\/3366423.3380297"},{"key":"2357_CR55","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Berg R, Titov I, Welling M (2017) Modeling relational data with graph convolutional networks","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"2357_CR56","doi-asserted-by":"crossref","unstructured":"Yang C, Pal A, Zhai A, Pancha N, Han J, Rosenberg C, Leskovec J (2020) Multisage: empowering gcn with contextualized multi-embeddings on web-scale multipartite networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2434\u20132443","DOI":"10.1145\/3394486.3403293"},{"key":"2357_CR57","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"2357_CR58","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Advances in neural information processing systems 32"},{"key":"2357_CR59","doi-asserted-by":"crossref","unstructured":"Hong H, Lin Y, Yang X, Li Z, Fu K, Wang Z, Qie X, Ye J (2020) Heteta: heterogeneous graph attention network. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 2444\u20132454","DOI":"10.1145\/3394486.3403294"},{"issue":"3","key":"2357_CR60","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1109\/TKDE.2020.2993870","volume":"34","author":"X Wang","year":"2022","unstructured":"Wang X, Lu Y, Shi C, Wang R, Cui P, Mou S (2022) Dynamic heterogeneous information network embedding with meta-path based proximity. IEEE Trans Knowl Data Eng 34(3):1117\u20131132. https:\/\/doi.org\/10.1109\/TKDE.2020.2993870","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2357_CR61","doi-asserted-by":"crossref","unstructured":"Bian R, Koh YS, Dobbie G, Divoli A (2019) Network embedding and change modeling in dynamic heterogeneous networks. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 861\u2013864","DOI":"10.1145\/3331184.3331273"},{"key":"2357_CR62","doi-asserted-by":"crossref","unstructured":"Milani Fard A, Bagheri E, Wang K (2019) Relationship prediction in dynamic heterogeneous information networks. In: European Conference on Information Retrieval, pp 19\u201334. Springer","DOI":"10.1007\/978-3-030-15712-8_2"},{"key":"2357_CR63","doi-asserted-by":"crossref","unstructured":"Xue H, Yang L, Jiang W, Wei Y, Hu Y, Lin Y (2020) Modeling dynamic heterogeneous network for link prediction using hierarchical attention with temporal rnn. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp 282\u2013298. Springer","DOI":"10.1007\/978-3-030-67658-2_17"},{"key":"2357_CR64","doi-asserted-by":"crossref","unstructured":"Yang L, Xiao Z, Jiang W, Wei Y, Hu Y, Wang H (2020) Dynamic heterogeneous graph embedding using hierarchical attentions. In: European Conference on Information Retrieval, pp 425\u2013432. Springer","DOI":"10.1007\/978-3-030-45442-5_53"},{"key":"2357_CR65","doi-asserted-by":"crossref","unstructured":"Li Q, Shang Y, Qiao X, Dai W (2020) Heterogeneous dynamic graph attention network. In: 2020 IEEE International Conference on Knowledge Graph (ICKG), pp 404\u2013411. IEEE","DOI":"10.1109\/ICBK50248.2020.00064"},{"issue":"3","key":"2357_CR66","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1111\/j.2517-6161.1971.tb01530.x","volume":"33","author":"AG Hawkes","year":"1971","unstructured":"Hawkes AG (1971) Point spectra of some mutually exciting point processes. J Royal Stat Soc: Ser B (Methodol) 33(3):438\u2013443","journal-title":"J Royal Stat Soc: Ser B (Methodol)"},{"key":"2357_CR67","doi-asserted-by":"publisher","first-page":"134782","DOI":"10.1109\/ACCESS.2019.2942221","volume":"7","author":"Y Yin","year":"2019","unstructured":"Yin Y, Ji L-X, Zhang J-P, Pei Y-L (2019) Dhne: network representation learning method for dynamic heterogeneous networks. IEEE Access 7:134782\u2013134792","journal-title":"IEEE Access"},{"key":"2357_CR68","unstructured":"Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26"},{"key":"2357_CR69","doi-asserted-by":"publisher","unstructured":"Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence, vol 28, no 1. https:\/\/doi.org\/10.1609\/aaai.v28i1.8870","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"2357_CR70","doi-asserted-by":"publisher","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. Proceedings of the AAAI Conference on Artificial Intelligence, vol 29, no 1. https:\/\/doi.org\/10.1609\/aaai.v29i1.9491","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"2357_CR71","doi-asserted-by":"crossref","unstructured":"Ji G, He S, Xu L, Liu K, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol 1: Long Papers), pp 687\u2013696","DOI":"10.3115\/v1\/P15-1067"},{"key":"2357_CR72","doi-asserted-by":"publisher","unstructured":"Kazemi SM, Poole D (2018) SimplE embedding for link prediction in knowledge graphs. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1802.04868 . https:\/\/arxiv.org\/abs\/1802.04868","DOI":"10.48550\/ARXIV.1802.04868"},{"key":"2357_CR73","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1016\/j.procs.2023.10.098","volume":"225","author":"K Ammar","year":"2023","unstructured":"Ammar K, Inoubli W, Zghal S, Borji A, Nguifo EM (2023) Trans-trip: translation-based embedding with triplets for heterogeneous graphs. Procedia Comput Sci 225:1104\u20131113","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"2357_CR74","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s00521-018-3728-2","volume":"31","author":"Z Huang","year":"2019","unstructured":"Huang Z, Shan G, Cheng J, Sun J (2019) Trec: an efficient recommendation system for hunting passengers with deep neural networks. Neural Comput Appl 31(1):209\u2013222. https:\/\/doi.org\/10.1007\/s00521-018-3728-2","journal-title":"Neural Comput Appl"},{"issue":"8","key":"2357_CR75","doi-asserted-by":"publisher","first-page":"4591","DOI":"10.1109\/TII.2019.2893714","volume":"15","author":"B Yi","year":"2019","unstructured":"Yi B, Shen X, Liu H, Zhang Z, Zhang W, Liu S, Xiong N (2019) Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Trans Ind Inform 15(8):4591\u20134601","journal-title":"IEEE Trans Ind Inform"},{"key":"2357_CR76","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.aiopen.2022.03.002","volume":"3","author":"J Liu","year":"2022","unstructured":"Liu J, Shi C, Yang C, Lu Z, Yu PS (2022) A survey on heterogeneous information network based recommender systems: concepts, methods, applications and resources. AI Open 3:40\u201357. https:\/\/doi.org\/10.1016\/j.aiopen.2022.03.002","journal-title":"AI Open"},{"key":"2357_CR77","doi-asserted-by":"crossref","unstructured":"Han X, Shi C, Wang S, Philip SY, Song L (2018) Aspect-level deep collaborative filtering via heterogeneous information networks. In: IJCAI, vol 18, pp 3393\u20133399","DOI":"10.24963\/ijcai.2018\/471"},{"key":"2357_CR78","doi-asserted-by":"publisher","unstructured":"Shi C, Hu B, Zhao WX, Yu PS (2017) Heterogeneous information network embedding for recommendation. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1711.10730 . https:\/\/arxiv.org\/abs\/1711.10730","DOI":"10.48550\/ARXIV.1711.10730"},{"key":"2357_CR79","doi-asserted-by":"crossref","unstructured":"Liu Z, Chen C, Yang X, Zhou J, Li X, Song L (2018) Heterogeneous graph neural networks for malicious account detection. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 2077\u20132085","DOI":"10.1145\/3269206.3272010"},{"issue":"01","key":"2357_CR80","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1609\/aaai.v33i01.3301946","volume":"33","author":"B Hu","year":"2019","unstructured":"Hu B, Zhang Z, Shi C, Zhou J, Li X, Qi Y (2019) Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism. Proc AAAI Conf Artif Intell 33(01):946\u2013953. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301946","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"3","key":"2357_CR81","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1005455","volume":"13","author":"Z-H You","year":"2017","unstructured":"You Z-H, Huang Z-A, Zhu Z, Yan G-Y, Li Z-W, Wen Z, Chen X (2017) Pbmda: a novel and effective path-based computational model for mirna-disease association prediction. PLOS Comput Biol 13(3):1\u201322. https:\/\/doi.org\/10.1371\/journal.pcbi.1005455","journal-title":"PLOS Comput Biol"},{"issue":"18","key":"2357_CR82","doi-asserted-by":"publisher","first-page":"3178","DOI":"10.1093\/bioinformatics\/bty333","volume":"34","author":"X Chen","year":"2018","unstructured":"Chen X, Xie D, Wang L, Zhao Q, You Z-H, Liu H (2018) Bnpmda: bipartite network projection for mirna-disease association prediction. Bioinformatics 34(18):3178\u20133186","journal-title":"Bioinformatics"},{"key":"2357_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-019-2009-x","volume":"17","author":"K Zheng","year":"2019","unstructured":"Zheng K, You Z-H, Wang L, Yong Z, Li L, Li Z-W (2019) Mlmda: a machine learning approach to predict and validate microrna-disease associations by integrating of heterogenous information sources. J Transl Med 17:1\u201314. https:\/\/doi.org\/10.1186\/s12967-019-2009-x","journal-title":"J Transl Med"},{"issue":"4","key":"2357_CR84","doi-asserted-by":"publisher","first-page":"849","DOI":"10.25300\/MISQ\/2016\/40.4.03","volume":"40","author":"K Zhang","year":"2016","unstructured":"Zhang K, Bhattacharyya S, Ram S (2016) Large-scale network analysis for online social brand advertising. Mis Q 40(4):849\u2013868","journal-title":"Mis Q"},{"key":"2357_CR85","doi-asserted-by":"publisher","first-page":"102117","DOI":"10.1016\/j.ijinfomgt.2020.102117","volume":"54","author":"J Vithayathil","year":"2020","unstructured":"Vithayathil J, Dadgar M, Osiri JK (2020) Social media use and consumer shopping preferences. Int J Inf Manag 54:102117. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2020.102117","journal-title":"Int J Inf Manag"},{"issue":"1","key":"2357_CR86","doi-asserted-by":"publisher","first-page":"13207","DOI":"10.1038\/s41598-021-92337-2","volume":"11","author":"T Radicioni","year":"2021","unstructured":"Radicioni T, Saracco F, Pavan E, Squartini T (2021) Analysing twitter semantic networks: the case of 2018 Italian elections. Sci Rep 11(1):13207","journal-title":"Sci Rep"},{"issue":"5","key":"2357_CR87","doi-asserted-by":"publisher","first-page":"3409","DOI":"10.1007\/s11280-023-01192-w","volume":"26","author":"R Corizzo","year":"2023","unstructured":"Corizzo R, Pio G, Barracchia EP, Pellicani A, Japkowicz N, Ceci M (2023) Huri: hybrid user risk identification in social networks. World Wide Web 26(5):3409\u20133439","journal-title":"World Wide Web"},{"key":"2357_CR88","doi-asserted-by":"publisher","unstructured":"Huang B, Raisi E (2018) In: Golbeck J (ed.) Weak supervision and machine learning for online harassment detection, pp 5\u201328. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-78583-7_2","DOI":"10.1007\/978-3-319-78583-7_2"},{"key":"2357_CR89","doi-asserted-by":"crossref","unstructured":"Gallagher B, Tong H, Eliassi-Rad T, Faloutsos C (2008) Using ghost edges for classification in sparsely labeled networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 256\u2013264","DOI":"10.1145\/1401890.1401925"},{"key":"2357_CR90","doi-asserted-by":"crossref","unstructured":"Uzel VN, E\u015fsiz ES, \u00d6zel SA (2018) Using fuzzy sets for detecting cyber terrorism and extremism in the text. In: 2018 Innovations in Intelligent Systems and Applications Conference (ASYU), pp 1\u20134. IEEE","DOI":"10.1109\/ASYU.2018.8554017"},{"key":"2357_CR91","doi-asserted-by":"crossref","unstructured":"Zhang Y, Gao S, Pei J, Huang H (2022) Improving social network embedding via new second-order continuous graph neural networks. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 2515\u20132523","DOI":"10.1145\/3534678.3539415"},{"key":"2357_CR92","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1016\/j.ins.2022.06.075","volume":"607","author":"S Kumar","year":"2022","unstructured":"Kumar S, Mallik A, Khetarpal A, Panda BS (2022) Influence maximization in social networks using graph embedding and graph neural network. Inf Sci 607:1617\u20131636. https:\/\/doi.org\/10.1016\/j.ins.2022.06.075","journal-title":"Inf Sci"},{"key":"2357_CR93","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.inffus.2022.11.029","volume":"92","author":"A Pellicani","year":"2023","unstructured":"Pellicani A, Pio G, Redavid D, Ceci M (2023) Sairus: spatially-aware identification of risky users in social networks. Inf Fusion 92:435\u2013449","journal-title":"Inf Fusion"},{"issue":"5","key":"2357_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447585","volume":"15","author":"H Peng","year":"2021","unstructured":"Peng H, Li J, Song Y, Yang R, Ranjan R, Yu PS, He L (2021) Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Trans Knowl Discov Data (TKDD) 15(5):1\u201333","journal-title":"ACM Trans Knowl Discov Data (TKDD)"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02357-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02357-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02357-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T12:37:13Z","timestamp":1747744633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02357-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":94,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2357"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02357-x","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"type":"print","value":"0219-1377"},{"type":"electronic","value":"0219-3116"}],"subject":[],"published":{"date-parts":[[2025,2,13]]},"assertion":[{"value":"26 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}