{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:36:08Z","timestamp":1777696568053,"version":"3.51.4"},"reference-count":143,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,2,3]]},"abstract":"<jats:p>Network data is ubiquitous, such as telecommunication, transport systems, online social networks, protein-protein interactions, etc. Since the huge scale and the complexity of network data, former machine learning system tried to understand network data arduously. On the other hand, thought of multi-granular cognitive computation simulates the problem-solving process of human brains. It simplifies the complex problems and solves problems from the easier to harder. Therefore, the application of multi-granularity problem-solving ideas or methods to deal with network data mining is increasingly adopted by researchers either intentionally or unintentionally. This paper looks into the domain of network representation learning (NRL). It systematically combs the research work in this field in recent years. In this paper, it is discovered that in dealing with the complexity of the network and pursuing the efficiency of computing resources, the multi-granularity solution becomes an excellent path that is hard to go around. Although there are several papers about survey of NRL, to our best knowledge, we are the first to survey the NRL from the perspective of multi-granular computing. This paper proposes the challenges that NRL meets. Furthermore, the feasibility of solving the challenges of NRL with multi-granular computing methodologies is analyzed and discussed. Some potential key scientific problems are sorted out and prospected in applying multi-granular computing for NRL research.<\/jats:p>","DOI":"10.3233\/ida-227328","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T06:41:03Z","timestamp":1701844863000},"page":"3-32","source":"Crossref","is-referenced-by-count":1,"title":["A review on network representation learning with multi-granularity perspective"],"prefix":"10.1177","volume":"28","author":[{"given":"Shun","family":"Fu","sequence":"first","affiliation":[{"name":"Chongqing Industry Polytechnic College, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lufeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Industry Polytechnic College, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Zunyi Normal University, Zunyi, Guizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/IDA-227328_ref1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1137\/S003614450342480","article-title":"The Structure and Function of Complex Networks","volume":"45","author":"Newman","year":"2003","journal-title":"SIAM Review"},{"key":"10.3233\/IDA-227328_ref2","doi-asserted-by":"crossref","unstructured":"G. Yang, H. Tao, R. Du and Y. Zhong, Compound Fault Diagnosis of Harmonic Drives Using Deep Capsule Graph Convolutional Network, IEEE Transactions on Industrial Electronics (2022).","DOI":"10.1109\/TIE.2022.3176280"},{"issue":"1","key":"10.3233\/IDA-227328_ref3","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MCAS.2003.1228503","article-title":"Complex networks: small-world, scale-free and beyond","volume":"3","author":"Wang","year":"2003","journal-title":"IEEE Circuits and Systems Magazine"},{"issue":"3","key":"10.3233\/IDA-227328_ref4","first-page":"2015025","article-title":"Network Representation Learning","volume":"1","author":"Chen","year":"2017","journal-title":"Big Data(in Chinese)"},{"key":"10.3233\/IDA-227328_ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM2005.125"},{"key":"10.3233\/IDA-227328_ref6","doi-asserted-by":"crossref","unstructured":"S. Wasserman and K. Faust, Social network analysis: Methods and applications, vol. 8. Cambridge university press, 1994.","DOI":"10.1017\/CBO9780511815478"},{"issue":"3","key":"10.3233\/IDA-227328_ref7","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/42.34715","article-title":"Detection of blood vessels in retinal images using two-dimensional matched filters","volume":"8","author":"Chaudhuri","year":"1989","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.3233\/IDA-227328_ref8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-1-4419-8462-3_5","article-title":"Node classification in social networks","author":"Bhagat","year":"2011","journal-title":"Social network data analytics"},{"key":"10.3233\/IDA-227328_ref9","first-page":"1201","article-title":"\u201cGraph kernels\u201d","volume":"11","author":"Vishwanathan","year":"2010","journal-title":"Journal of Machine Learning Research"},{"issue":"7","key":"10.3233\/IDA-227328_ref10","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1002\/asi.20591","article-title":"The link-prediction problem for social networks","volume":"58","author":"Liben-Nowell","year":"2007","journal-title":"Journal of the American Society for Information Science and Technology"},{"issue":"2","key":"10.3233\/IDA-227328_ref11","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s10462-016-9462-1","article-title":"Influence of social network on method musical composition","volume":"46","author":"Mora-Guti\u00e9rrez","year":"2016","journal-title":"Artificial Intelligence Review"},{"issue":"6","key":"10.3233\/IDA-227328_ref12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Processing Magazine"},{"issue":"5786","key":"10.3233\/IDA-227328_ref13","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"issue":"4","key":"10.3233\/IDA-227328_ref14","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1080\/107985872012.10643246","article-title":"Development of a Hybrid Artificial Neural Network Model and its Application to Data Regression","volume":"18","author":"Lee","year":"2012","journal-title":"Intelligent Automation & Soft Computing"},{"issue":"4","key":"10.3233\/IDA-227328_ref15","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/107985872015.1008735","article-title":"Simple and Computationally Efficient Movement Classification Approach for EMG-controlled Prosthetic Hand: ANFIS vs. Artificial Neural Network","volume":"21","author":"Jahani Fariman","year":"2015","journal-title":"Intelligent Automation & Soft Computing"},{"issue":"3\u20134","key":"10.3233\/IDA-227328_ref16","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1561\/0400000025","article-title":"Spectral algorithms","volume":"4","author":"Kannan","year":"2009","journal-title":"Foundations and Trends\u00ae in Theoretical Computer Science"},{"key":"10.3233\/IDA-227328_ref17","unstructured":"M. Brand and K. Huang, A unifying theorem for spectral embedding and clustering, in AISTATS, 2003."},{"key":"10.3233\/IDA-227328_ref18","doi-asserted-by":"crossref","unstructured":"T.M. Le and H.W. Lauw, Probabilistic latent document network embedding, in IEEE International Conference on Data Mining, 2014. pp.\u00a0270\u2013279.","DOI":"10.1109\/ICDM.2014.119"},{"key":"10.3233\/IDA-227328_ref19","doi-asserted-by":"crossref","unstructured":"Y. Jacob, L. Denoyer and P. Gallinari, Learning latent representations of nodes for classifying in heterogeneous social networks, in Proceedings of the 7th ACM international conference on Web search and data mining, 2014, pp. 373\u2013382.","DOI":"10.1145\/2556195.2556225"},{"key":"10.3233\/IDA-227328_ref20","doi-asserted-by":"crossref","unstructured":"J. Yang and J. Leskovec, Modeling information diffusion in implicit networks, in Data Mining (ICDM), 2010 IEEE 10th International Conference on, 2010, pp.\u00a0599\u2013608.","DOI":"10.1109\/ICDM.2010.22"},{"key":"10.3233\/IDA-227328_ref21","doi-asserted-by":"crossref","unstructured":"S. Bourigault, C. Lagnier, S. Lamprier, L. Denoyer and P. Gallinari, Learning social network embeddings for predicting information diffusion, in Proceedings of the 7th ACM international conference on Web search and data mining, 2014, pp.\u00a0393\u2013402.","DOI":"10.1145\/2556195.2556216"},{"key":"10.3233\/IDA-227328_ref22","doi-asserted-by":"crossref","unstructured":"T. Hofmann, Probabilistic latent semantic indexing, in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 1999, pp.\u00a050\u201357.","DOI":"10.1145\/312624.312649"},{"key":"10.3233\/IDA-227328_ref23","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/IDA-227328_ref24","doi-asserted-by":"crossref","unstructured":"R.M. Nallapati, A. Ahmed, E.P. Xing and W.W. Cohen, Joint latent topic models for text and citations, in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp.\u00a0542\u2013550.","DOI":"10.1145\/1401890.1401957"},{"key":"10.3233\/IDA-227328_ref25","first-page":"81","article-title":"Relational Topic Models for Document Networks","author":"Chang","year":"2009","journal-title":"Artificial Intelligence and Statistics"},{"key":"10.3233\/IDA-227328_ref26","unstructured":"T. Mikolov, K. Chen, G. Corrado and J. Dean, Efficient Estimation of Word Representations in Vector Space, in In Workshop Track Proceedings of International Conference on Learning Representations, Scottsdale, Arizona, USA, 2013."},{"key":"10.3233\/IDA-227328_ref27","unstructured":"T. Mikolov, W. Yih and G. Zweig, Linguistic regularities in continuous space word representations, in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2013, pp.\u00a0746\u2013751."},{"key":"10.3233\/IDA-227328_ref28","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov","year":"2013","journal-title":"Advances in neural information processing systems"},{"key":"10.3233\/IDA-227328_ref29","doi-asserted-by":"crossref","unstructured":"B. Perozzi, R. Al-Rfou and S. Skiena, Deepwalk: Online learning of social representationsin, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp.\u00a0701\u2013710.","DOI":"10.1145\/2623330.2623732"},{"key":"10.3233\/IDA-227328_ref30","doi-asserted-by":"crossref","unstructured":"J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan and Q. Mei, Line: Large-scale information network embedding, in Proceedings of the 24th International Conference on World Wide Web, Florence, 2015, pp.\u00a01067\u20131077.","DOI":"10.1145\/2736277.2741093"},{"key":"10.3233\/IDA-227328_ref31","doi-asserted-by":"crossref","unstructured":"J. Tang, M. Qu and Q. Mei, 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, 2015, pp.\u00a01165\u20131174.","DOI":"10.1145\/2783258.2783307"},{"key":"10.3233\/IDA-227328_ref32","doi-asserted-by":"crossref","unstructured":"A. Grover and J. Leskovec, node2vec Scalable Feature Learning for Networks, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13\u201317, 2016, San Francisco, 2016, pp.\u00a0855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"issue":"8","key":"10.3233\/IDA-227328_ref33","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"10.3233\/IDA-227328_ref34","doi-asserted-by":"crossref","unstructured":"J. Li, C. Chen, H. Tong and H. Liu, Multi-layered network embedding, in Proceedings of the 2018 SIAM International Conference on Data Mining, 2018, pp.\u00a0684\u2013692.","DOI":"10.1137\/1.9781611975321.77"},{"key":"10.3233\/IDA-227328_ref35","doi-asserted-by":"crossref","unstructured":"S. Cao, W. Lu and Q. Xu, Grarep: Learning graph representations with global structural information, in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, pp.\u00a0891\u2013900.","DOI":"10.1145\/2806416.2806512"},{"key":"10.3233\/IDA-227328_ref36","doi-asserted-by":"crossref","unstructured":"N. Liu, X. Huang, J. Li and X. Hu, On Interpretation of Network Embedding via Taxonomy Induction, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp.\u00a01812\u20131820.","DOI":"10.1145\/3219819.3220001"},{"key":"10.3233\/IDA-227328_ref37","doi-asserted-by":"crossref","unstructured":"J. Ma, P. Cui, X. Wang and W. Zhu, Hierarchical taxonomy aware network embedding, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp.\u00a01920\u20131929.","DOI":"10.1145\/3219819.3220062"},{"issue":"6","key":"10.3233\/IDA-227328_ref38","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TSMCC.2012.2236648","article-title":"Granular computing: perspectives and challenges","volume":"43","author":"Yao","year":"2013","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.3233\/IDA-227328_ref39","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/GRC.2005.1547239","article-title":"Perspectives of granular computing","volume":"1","author":"Yao","year":"2005","journal-title":"2005 IEEE international conference on granular computing"},{"issue":"4","key":"10.3233\/IDA-227328_ref40","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s41066-017-0048-3","article-title":"DGCC: data-driven granular cognitive computing","volume":"2","author":"Wang","year":"2017","journal-title":"Granular Computing"},{"key":"10.3233\/IDA-227328_ref41","unstructured":"G.J. Klir and B. Yuan, Fuzzy sets, fuzzy logic and fuzzy systems: selected papers by Lotfi A. Zadeh. World Scientific Publishing Co., Inc., 1996."},{"issue":"2","key":"10.3233\/IDA-227328_ref42","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0165-0114(97)00077-8","article-title":"Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic","volume":"90","author":"Zadeh","year":"1997","journal-title":"Fuzzy Sets and Systems"},{"key":"10.3233\/IDA-227328_ref43","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ins.2016.08.086","article-title":"DenPEHC: Density peak based efficient hierarchical clustering","volume":"373","author":"Xu","year":"2016","journal-title":"Information Sciences"},{"issue":"3","key":"10.3233\/IDA-227328_ref44","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1080\/107985872008.10643243","article-title":"Multi-Connect Architecture (MCA) Associative Memory: A Modified Hopfield Neural Network","volume":"18","author":"Kareem","year":"2012","journal-title":"Intelligent Automation & Soft Computing"},{"issue":"1\u20134","key":"10.3233\/IDA-227328_ref45","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3233\/FI-2013-916","article-title":"Granular computing based on gaussian cloud transformation","volume":"127","author":"Liu","year":"2013","journal-title":"Fundamenta Informaticae"},{"key":"10.3233\/IDA-227328_ref46","doi-asserted-by":"crossref","unstructured":"S. Poria, E. Cambria and A. Gelbukh, Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis, in Proceedings of the 2015 conference on empirical methods in natural language processing, 2015, 2539\u20132544.","DOI":"10.18653\/v1\/D15-1303"},{"key":"10.3233\/IDA-227328_ref47","doi-asserted-by":"crossref","unstructured":"C. Yang, M. Sun, Z. Liu and C. Tu, Fast network embedding enhancement via high order proximity approximation, in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI, 2017. pp.\u00a019\u201325.","DOI":"10.24963\/ijcai.2017\/544"},{"key":"10.3233\/IDA-227328_ref48","doi-asserted-by":"crossref","unstructured":"H. Chen, B. Perozzi, Y. Hu and S. Skiena, Harp: Hierarchical representation learning for networks, in Proceedings of the AAAI conference on artificial intelligence 32(1) (2018), 2127\u20132134.","DOI":"10.1609\/aaai.v32i1.11849"},{"key":"10.3233\/IDA-227328_ref49","doi-asserted-by":"publisher","DOI":"10.1145\/32198193219969"},{"key":"10.3233\/IDA-227328_ref50","doi-asserted-by":"crossref","unstructured":"S. Zhang, H. Tong, R. Maciejewski and T. Eliassi-Rad, Multilevel Network Alignment, presented at the International World Wide Web Conference Committee, 2019.","DOI":"10.1145\/3308558.3313484"},{"key":"10.3233\/IDA-227328_ref51","doi-asserted-by":"crossref","unstructured":"B. Perozzi, V. Kulkarni, H. Chen and S. Skiena, Dson\u2019t Walk, Skip!: Online Learning of Multi-scale Network Embeddings, in Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017, pp.\u00a0258\u2013265.","DOI":"10.1145\/3110025.3110086"},{"key":"10.3233\/IDA-227328_ref52","unstructured":"B. Perozzi, V. Kulkarni and S. Skiena, Walklets: Multiscale Graph Embeddings for Interpretable Network Classification, p.\u00a016."},{"issue":"7191","key":"10.3233\/IDA-227328_ref53","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/nature06830","article-title":"Hierarchical structure and the prediction of missing links in networks","volume":"453","author":"Clauset","year":"2008","journal-title":"Nature"},{"issue":"9","key":"10.3233\/IDA-227328_ref54","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications","volume":"30","author":"Cai","year":"2018","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"3","key":"10.3233\/IDA-227328_ref55","first-page":"52","article-title":"Representation Learning on Graphs: Methods and Applications","volume":"40","author":"Hamilton","year":"2017","journal-title":"IEEE Data Engineering Bulletin"},{"key":"10.3233\/IDA-227328_ref56","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","author":"Belkin","year":"2002","journal-title":"Advances in neural information processing systems"},{"key":"10.3233\/IDA-227328_ref57","doi-asserted-by":"crossref","unstructured":"S. Abu-El-Haija, B. Perozzi and R. Al-Rfou, Learning edge representations via low-rank asymmetric projections, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp.\u00a01787\u20131796.","DOI":"10.1145\/3132847.3132959"},{"key":"10.3233\/IDA-227328_ref58","doi-asserted-by":"crossref","unstructured":"H. Bunke, C. Irniger and M. Neuhaus, Graph matching\u2013challenges and potential solutions, in International Conference on Image Analysis and Processing, 2005. pp.\u00a01\u201310.","DOI":"10.1007\/11553595_1"},{"key":"10.3233\/IDA-227328_ref59","doi-asserted-by":"crossref","unstructured":"H. Bunke, S. G\u00fcnter and X. Jiang, Towards bridging the gap between statistical and structural pattern recognition: Two new concepts in graph matching, in International Conference on Advances in Pattern Recognition, 2001. pp.\u00a01\u201311.","DOI":"10.1007\/3-540-44732-6_1"},{"issue":"12","key":"10.3233\/IDA-227328_ref60","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/TPAMI.2003.1251147","article-title":"Optimal cluster preserving embedding of nonmetric proximity data","volume":"25","author":"Roth","year":"2003","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/IDA-227328_ref61","unstructured":"M. Chen, Q. Yang and X. Tang, Directed graph embedding, in Proceedings of the 20th international joint conference on Artifical intelligence, 2007, pp.\u00a02707\u20132712."},{"issue":"3","key":"10.3233\/IDA-227328_ref62","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1142\/S0218001404003228","article-title":"Thirty years of graph matching in pattern recognition","volume":"18","author":"Conte","year":"2004","journal-title":"International Journal of Pattern Recognition and Artificial Intelligence"},{"key":"10.3233\/IDA-227328_ref63","doi-asserted-by":"crossref","unstructured":"G. Lee and A. Madabhushi, Semi-supervised graph embedding scheme with active learning (SSGEAL): classifying high dimensional biomedical data, in IAPR International Conference on Pattern Recognition in Bioinformatics, 2010. pp.\u00a0207\u2013218.","DOI":"10.1007\/978-3-642-16001-1_18"},{"key":"10.3233\/IDA-227328_ref64","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/978-3-642-17711-8_10","article-title":"A fuzzy-interval based approach for explicit graph embedding","author":"Luqman","year":"2010","journal-title":"Recognizing patterns in signals, speech, images and videos"},{"key":"10.3233\/IDA-227328_ref65","first-page":"117","article-title":"Dimensionality Reduction for Fuzzy-Interval Based Explicit Graph Embedding","volume":"9","author":"Luqman","year":"2011","journal-title":"Ninth IAPR International Workshop on Graphics RECognition"},{"issue":"2","key":"10.3233\/IDA-227328_ref66","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.patcog.2012.07.029","article-title":"Fuzzy multilevel graph embedding","volume":"46","author":"Luqman","year":"2013","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-227328_ref67","first-page":"81","article-title":"A comparison of explicit and implicit graph embedding methods for pattern recognition","author":"Conte","year":"2013","journal-title":"International Workshop on Graph-Based Representations in Pattern Recognition"},{"key":"10.3233\/IDA-227328_ref68","unstructured":"S. Yan, D. Xu, B. Zhang and H.-J. Zhang, Graph embedding: A general framework for dimensionality reduction, in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on 2 (2005), 830\u2013837."},{"key":"10.3233\/IDA-227328_ref69","doi-asserted-by":"crossref","unstructured":"J. Qiu, Y. Dong, H. Ma, J. Li, K. Wang and J. Tang, Network embedding as matrix factorization: Unifying deepwalk, line, pte and node2vec, in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018, pp.\u00a0459\u2013467.","DOI":"10.1145\/3159652.3159706"},{"key":"10.3233\/IDA-227328_ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE2018.2849727"},{"key":"10.3233\/IDA-227328_ref71","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-1-4757-1904-8_7","article-title":"Principal component analysis and factor analysis","author":"Jolliffe","year":"1986","journal-title":"Principal component analysis"},{"issue":"5500","key":"10.3233\/IDA-227328_ref72","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"issue":"5500","key":"10.3233\/IDA-227328_ref73","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"10.3233\/IDA-227328_ref74","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.knosys.2017.07.031","article-title":"A unified model of sequential three-way decisions and multilevel incremental processing","volume":"134","author":"Yang","year":"2017","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-227328_ref75","doi-asserted-by":"crossref","unstructured":"M. Girvan and M.E. Newman, Community structure in social and biological networks, Proceedings of the national academy of sciences 99(12) (2002), 7821\u20137826.","DOI":"10.1073\/pnas.122653799"},{"issue":"2","key":"10.3233\/IDA-227328_ref76","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1140\/epjb\/e2004-00124-y","article-title":"Detecting community structure in networks","volume":"38","author":"Newman","year":"2004","journal-title":"The European Physical Journal B"},{"key":"10.3233\/IDA-227328_ref77","unstructured":"V. Alessandro and C. Guido, Large scale structure and dynamics of complex networks: from information technology to finance and natural science Vol.\u00a02. World Scientific, 2007."},{"key":"10.3233\/IDA-227328_ref78","doi-asserted-by":"crossref","unstructured":"R.A. Meyers, Encyclopedia of complexity and systems science. Springer, 2009.","DOI":"10.1007\/978-3-642-27737-5"},{"issue":"5980","key":"10.3233\/IDA-227328_ref79","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1126\/science.1184819","article-title":"Community structure in time-dependent, multiscale and multiplex networks","volume":"328","author":"Mucha","year":"2010","journal-title":"Science"},{"issue":"3","key":"10.3233\/IDA-227328_ref80","doi-asserted-by":"crossref","first-page":"033015","DOI":"10.1088\/1367-2630\/11\/3\/033015","article-title":"Detecting the overlapping and hierarchical community structure in complex networks","volume":"11","author":"Lancichinetti","year":"2009","journal-title":"New Journal of Physics"},{"issue":"7307","key":"10.3233\/IDA-227328_ref81","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1038\/nature09182","article-title":"Link communities reveal multiscale complexity in networks","volume":"466","author":"Ahn","year":"2010","journal-title":"Nature"},{"issue":"4","key":"10.3233\/IDA-227328_ref82","doi-asserted-by":"crossref","first-page":"e18209","DOI":"10.1371\/journal.pone.0018209","article-title":"Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems","volume":"6","author":"Rosvall","year":"2011","journal-title":"PLoS ONE"},{"issue":"4","key":"10.3233\/IDA-227328_ref83","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10462-016-9488-4","article-title":"Weighted-spectral clustering algorithm for detecting community structures in complex networks","volume":"47","author":"Wang","year":"2017","journal-title":"Artificial Intelligence Review"},{"key":"10.3233\/IDA-227328_ref84","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","author":"Lee","year":"2001","journal-title":"Advances in neural information processing systems"},{"issue":"3","key":"10.3233\/IDA-227328_ref85","doi-asserted-by":"crossref","first-page":"036104","DOI":"10.1103\/PhysRevE.74.036104","article-title":"Finding community structure in networks using the eigenvectors of matrices","volume":"74","author":"Newman","year":"2006","journal-title":"Physical review E"},{"key":"10.3233\/IDA-227328_ref86","first-page":"2111","article-title":"Network representation learning with rich text information","author":"Yang","year":"2015","journal-title":"IJCAI"},{"key":"10.3233\/IDA-227328_ref87","doi-asserted-by":"crossref","unstructured":"E. Akbas and P. Zhao, Attributed Graph Clustering: an Attribute-aware Graph Embedding Approach, in Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017, pp.\u00a0305\u2013308.","DOI":"10.1145\/3110025.3110092"},{"key":"10.3233\/IDA-227328_ref88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","author":"Dempster","year":"1977","journal-title":"Journal of the royal statistical society Series B (methodological)"},{"key":"10.3233\/IDA-227328_ref89","first-page":"3889","article-title":"Max-Margin DeepWalk: Discriminative Learning of Network Representation","author":"Tu","year":"2016","journal-title":"IJCAI"},{"key":"10.3233\/IDA-227328_ref90","doi-asserted-by":"crossref","unstructured":"C. Tu, H. Liu, Z. Liu and M. Sun, Cane: Context-aware network embedding for relation modeling, in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 1 (2017), 1722\u20131731.","DOI":"10.18653\/v1\/P17-1158"},{"key":"10.3233\/IDA-227328_ref91","doi-asserted-by":"crossref","unstructured":"Y. Lu, C. Shi, L. Hu and Z. Liu, Relation Structure-Aware Heterogeneous Information Network Embedding, in Thirty-Third AAAI Conference on Artificial Intelligence, 2019.","DOI":"10.1609\/aaai.v33i01.33014456"},{"key":"10.3233\/IDA-227328_ref92","unstructured":"S. Pan, J. Wu, X. Zhu, C. Zhang and Y. Wang, Tri-party deep network representation, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016. pp.\u00a01895\u20131901."},{"key":"10.3233\/IDA-227328_ref93","doi-asserted-by":"crossref","unstructured":"X. Huang, J. Li and X. Hu, Label informed attributed network embedding, in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017. pp.\u00a0731\u2013739.","DOI":"10.1145\/3018661.3018667"},{"key":"10.3233\/IDA-227328_ref94","unstructured":"W. Hamilton, Z. Ying and J. Leskovec, Inductive representation learning on large graphs, in Advances in Neural Information Processing Systems, 2017. pp.\u00a01024\u20131034."},{"key":"10.3233\/IDA-227328_ref95","unstructured":"T.N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv1609.02907 2016."},{"key":"10.3233\/IDA-227328_ref96","unstructured":"T.N. Kipf and M. Welling, Variational graph auto-encoders, arXiv preprint arXiv1611.07308. 2016."},{"key":"10.3233\/IDA-227328_ref97","doi-asserted-by":"crossref","unstructured":"M. Schlichtkrull, T.N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling, Modeling relational data with graph convolutional networks, in European Semantic Web Conference, 2018. pp.\u00a0593\u2013607.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"10.3233\/IDA-227328_ref98","unstructured":"R. van den Berg, T.N. Kipf and M. Welling, Graph convolutional matrix completion, arXiv preprint arXiv1706.02263. 2017."},{"issue":"2","key":"10.3233\/IDA-227328_ref99","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1145\/2503792.2503797","article-title":"Information diffusion in online social networks: A survey","volume":"42","author":"Guille","year":"2013","journal-title":"ACM Sigmod Record"},{"key":"10.3233\/IDA-227328_ref100","doi-asserted-by":"crossref","unstructured":"J. Cheng, L. Adamic, P.A. Dow, J.M. Kleinberg and J. Leskovec, Can cascades be predicted, in Proceedings of the 23rd international conference on World wide web, 2014, pp.\u00a0925\u2013936.","DOI":"10.1145\/2566486.2567997"},{"key":"10.3233\/IDA-227328_ref101","doi-asserted-by":"crossref","unstructured":"D. Wang, P. Cui and W. Zhu, Structural deep network embedding, in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp.\u00a01225\u20131234.","DOI":"10.1145\/2939672.2939753"},{"issue":"4","key":"10.3233\/IDA-227328_ref102","doi-asserted-by":"publisher","first-page":"395","DOI":"10.3233\/FI-2009-0026","article-title":"3DM: Domain-oriented Data-driven Data Mining","volume":"90","author":"Wang","year":"2009","journal-title":"Fundamenta Informaticae"},{"issue":"2","key":"10.3233\/IDA-227328_ref103","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1023\/A:1025401527570","article-title":"On cognitive informatics","volume":"4","author":"Wang","year":"2003","journal-title":"Brain and Mind"},{"key":"10.3233\/IDA-227328_ref104","doi-asserted-by":"crossref","unstructured":"J. Leskovec, L. Backstrom and J. Kleinberg, Meme-tracking and the dynamics of the news cycle, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp.\u00a0497\u2013506.","DOI":"10.1145\/1557019.1557077"},{"issue":"4","key":"10.3233\/IDA-227328_ref105","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1023\/A:1024940629314","article-title":"Bursty and hierarchical structure in streams","volume":"7","author":"Kleinberg","year":"2003","journal-title":"Data Mining and Knowledge Discovery"},{"key":"10.3233\/IDA-227328_ref106","doi-asserted-by":"publisher","DOI":"10.1145\/30389123052643"},{"issue":"4","key":"10.3233\/IDA-227328_ref107","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/72.80266","article-title":"The multilayer perceptron as an approximation to a Bayes optimal discriminant function","volume":"1","author":"Ruck","year":"1990","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"2","key":"10.3233\/IDA-227328_ref108","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s100510050250","article-title":"Direct causal cascade in the stock market","volume":"2","author":"Arneodo","year":"1998","journal-title":"The European Physical Journal B-Condensed Matter and Complex Systems"},{"issue":"3","key":"10.3233\/IDA-227328_ref109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/jdwm.2007070101","article-title":"Multi-label classification: An overview","volume":"3","author":"Tsoumakas","year":"2007","journal-title":"International Journal of Data Warehousing and Mining (IJDWM)"},{"key":"10.3233\/IDA-227328_ref110","doi-asserted-by":"crossref","unstructured":"S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng and H. Zha, Like like alike: joint friendship and interest propagation in social networks, in Proceedings of the 20th international conference on World wide web, 2011, pp.\u00a0537\u2013546.","DOI":"10.1145\/1963405.1963481"},{"key":"10.3233\/IDA-227328_ref111","doi-asserted-by":"crossref","unstructured":"L. Xu, X. Wei, J. Cao and P.S. Yu, Embedding identity and interest for social networks, in Proceedings of the 26th International Conference on World Wide Web Companion, 2017, pp.\u00a0859\u2013860.","DOI":"10.1145\/3041021.3054268"},{"key":"10.3233\/IDA-227328_ref112","unstructured":"P. Radivojac et\u00a0al., A large-scale evaluation of computational protein function prediction, Nature Methods 10(3) (2013)."},{"issue":"6","key":"10.3233\/IDA-227328_ref113","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","article-title":"Link prediction in complex networks: A survey","volume":"390","author":"L\u00fc","year":"2011","journal-title":"Physica A: Statistical Mechanics and Its Applications"},{"issue":"6","key":"10.3233\/IDA-227328_ref114","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1038\/nbt825","article-title":"Global protein function prediction from protein-protein interaction networks","volume":"21","author":"Vazquez","year":"2003","journal-title":"Nature biotechnology"},{"key":"10.3233\/IDA-227328_ref115","doi-asserted-by":"crossref","unstructured":"L. Backstrom and J. Leskovec, Supervised random walks: predicting and recommending links in social networks, in Proceedings of the fourth ACM international conference on Web search and data mining, 2011. pp.\u00a0635\u2013644.","DOI":"10.1145\/1935826.1935914"},{"issue":"2","key":"10.3233\/IDA-227328_ref116","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/1117454.1117456","article-title":"Link mining: a survey","volume":"7","author":"Getoor","year":"2005","journal-title":"Acm Sigkdd Explorations Newsletter"},{"key":"10.3233\/IDA-227328_ref117","unstructured":"J. Tang, J. Sun, C. Wang and Z. Yang, Blogs as a collective intelligence community, in Proceedings of the 2009 ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp.\u00a077\u201386."},{"key":"10.3233\/IDA-227328_ref118","doi-asserted-by":"crossref","unstructured":"C.L. Giles, K.D. Bollacker and S. Lawrence, CiteSeer: An automatic citation indexing system, in Proceedings of the third ACM conference on Digital libraries, 1998. pp.\u00a089\u201398.","DOI":"10.1145\/276675.276685"},{"key":"10.3233\/IDA-227328_ref119","unstructured":"C. Ding, A tutorial on spectral clustering, in Talk presented at ICML(Slides. available at http:\/\/crd.lbl.gov\/cding\/Spectral\/), 2004."},{"issue":"3","key":"10.3233\/IDA-227328_ref120","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0378-8733(78)90021-7","article-title":"Centrality in social networks conceptual clarification","volume":"1","author":"Freeman","year":"1978","journal-title":"Social networks"},{"issue":"4","key":"10.3233\/IDA-227328_ref121","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s10462-012-9325-3","article-title":"Detecting network communities using regularized spectral clustering algorithm","volume":"41","author":"Huang","year":"2014","journal-title":"Artificial Intelligence Review"},{"issue":"455","key":"10.3233\/IDA-227328_ref122","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1198\/016214501753208735","article-title":"Estimation and prediction for stochastic blockstructures","volume":"96","author":"Nowicki","year":"2001","journal-title":"Journal of the American statistical association"},{"key":"10.3233\/IDA-227328_ref123","unstructured":"C. Kemp, T.L. Griffiths and J.B. Tenenbaum, Discovering latent classes in relational data, CSAIL Technical Reports, 2004."},{"key":"10.3233\/IDA-227328_ref124","first-page":"75","article-title":"Playing multiple roles: Discovering overlapping roles in social networks","author":"Wolfe","year":"2004","journal-title":"ICML-04 workshop on statistical relational learning and its connections to other fields"},{"key":"10.3233\/IDA-227328_ref125","doi-asserted-by":"crossref","unstructured":"J. Adibi, H. Chalupsky, E. Melz and A. Valente, The KOJAK group finder: Connecting the dots via integrated knowledge-based and statistical reasoning, in Proceedings of the national conference on Artificial Intelligence, 2004. pp.\u00a0800\u2013807.","DOI":"10.21236\/ADA459397"},{"key":"10.3233\/IDA-227328_ref126","unstructured":"J.K. Carnegie, J. Kubica, A. Moore and J. Schneider, Tractable Group Detection on Large Link Data Sets, in The third IEEE international conference on data mining, 2003."},{"key":"10.3233\/IDA-227328_ref127","unstructured":"J. Kubica, A. Moore, J. Schneider and Y. Yang, Stochastic link and group detection, in Proceedings of the national conference on Artificial Intelligence, 2002. pp.\u00a0798\u2013806."},{"key":"10.3233\/IDA-227328_ref128","doi-asserted-by":"crossref","unstructured":"X. Wang, N. Mohanty and A. McCallum, Group and topic discovery from relations and text, in Proceedings of the 3rd international workshop on Link discovery, 2005, pp.\u00a028\u201335.","DOI":"10.1145\/1134271.1134276"},{"key":"10.3233\/IDA-227328_ref129","unstructured":"J. MacQueen, Some methods for classification and analysis of multivariate observations, in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability 1 (1967), 281\u2013297."},{"key":"10.3233\/IDA-227328_ref131","doi-asserted-by":"crossref","unstructured":"S. Cao, W. Lu and Q. Xu, Deep neural networks for learning graph representations, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016. pp.\u00a01145\u20131152.","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"10.3233\/IDA-227328_ref132","doi-asserted-by":"crossref","unstructured":"Z. Huang, Y. Zheng, R. Cheng, Y. Sun, N. Mamoulis and X. Li, Meta structure: Computing relevance in large heterogeneous information networks, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp.\u00a01595\u20131604.","DOI":"10.1145\/2939672.2939815"},{"key":"10.3233\/IDA-227328_ref133","doi-asserted-by":"crossref","unstructured":"K. Bollacker, C. Evans, P. Paritosh, T. Sturge and J. Taylor, Freebase: a collaboratively created graph database for structuring human knowledge, in Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008, pp.\u00a01247\u20131250.","DOI":"10.1145\/1376616.1376746"},{"issue":"2008","key":"10.3233\/IDA-227328_ref134","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/IDA-227328_ref135","unstructured":"K.W. Lim and W. Buntine, Bibliographic Analysis with the Citation Network Topic Model, in Asian Conference on Machine Learning, 2015, pp.\u00a0142\u2013158."},{"key":"10.3233\/IDA-227328_ref136","doi-asserted-by":"crossref","unstructured":"M. Xie, H. Yin, H. Wang, F. Xu, W. Chen and S. Wang, Learning graph-based poi embedding for location-based recommendation, in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016, pp.\u00a015\u201324.","DOI":"10.1145\/2983323.2983711"},{"issue":"2","key":"10.3233\/IDA-227328_ref137","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s10462-011-9222-1","article-title":"The state-of-the-art in personalized recommender systems for social networking","volume":"37","author":"Zhou","year":"2012","journal-title":"Artificial Intelligence Review"},{"key":"10.3233\/IDA-227328_ref138","doi-asserted-by":"crossref","unstructured":"S. Bourigault, S. Lamprier and P. Gallinari, Representation learning for information diffusion through social networks: an embedded cascade model, in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016. pp.\u00a0573\u2013582.","DOI":"10.1145\/2835776.2835817"},{"key":"10.3233\/IDA-227328_ref139","unstructured":"J. Feng, M. Huang and Y. Yang, GAKE: Graph aware knowledge embeddingin, Proceedings of COLING 2016 the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp.\u00a0641\u2013651."},{"key":"10.3233\/IDA-227328_ref140","unstructured":"L. Liu, W.K. Cheung, X. Li and L. Liao, Aligning Users Across Social Networks Using Network Embedding, in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016. pp.\u00a01774\u20131780."},{"key":"10.3233\/IDA-227328_ref141","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS2019.2900095","article-title":"ABNE: An Attention Based Network Embedding for User Alignment Across Social Networks","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/IDA-227328_ref142","doi-asserted-by":"crossref","unstructured":"S. Bandyopadhyay, L. N, and M.N. Murty, Outlier Aware Network Embedding for Attributed Networks, in Thirty-Third AAAI Conference on Artificial Intelligence, 2019.","DOI":"10.1609\/aaai.v33i01.330112"},{"issue":"2","key":"10.3233\/IDA-227328_ref143","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10462-011-9250-x","article-title":"Incremental K-clique clustering in dynamic social networks","volume":"38","author":"Duan","year":"2012","journal-title":"Artificial Intelligence Review"},{"key":"10.3233\/IDA-227328_ref144","doi-asserted-by":"crossref","unstructured":"X. Sun, Z. Song, J. Dong, Y. Yu, C. Plant and C. B\u00f6hm, Network Structure and Transfer Behaviors Embedding via Deep Prediction Model, in Thirty-Third AAAI Conference on Artificial Intelligence, 2019.","DOI":"10.1609\/aaai.v33i01.33015041"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-227328","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:20:22Z","timestamp":1777454422000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-227328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,3]]},"references-count":143,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/ida-227328","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,3]]}}}