{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:09:32Z","timestamp":1740175772576,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81960915"],"award-info":[{"award-number":["81960915"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Network representation learning aims to map the relationship between network nodes and context nodes to a low-dimensional representation vector space. Directed network representation learning considers mapping directional of node vector. Currently, only sporadic work on direct network representation has been reported. In this work, we propose a novel algorithm that takes into account the direction of the edge with text\u2019s attribute of the node in directed network representation learning. We then define the matrix based on in-degree of Laplacian and signless Laplacian for digraph, and it utilizes web page datasets from universities in the USA to evaluate the performance of vertex classification. We compare our algorithm with other directed representation learning algorithms. The experimental results show that our algorithm outperforms the baseline by over 20% when the training ratio ranges from 10 to 90%. We apply the in-degree-Laplacian and In-degree-signless-Laplacian to directed representation learning, which is one of the main contributions of this algorithm. Additionally, we incorporate text information through matrix completion in directed network representation learning and the experimental results show an increase in performance of up to 20% compared to the baseline, especially when the training ratio is 10%.<\/jats:p>","DOI":"10.1007\/s40747-024-01435-x","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T08:01:53Z","timestamp":1714377713000},"page":"5379-5390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Representation learning of in-degree-based digraph with rich information"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9300-3854","authenticated-orcid":false,"given":"Yan","family":"Sun","sequence":"first","affiliation":[]},{"given":"Cun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"JianFu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kejia","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Jiuchang","family":"Pei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"issue":"1","key":"1435_CR1","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1109\/TBDATA.2022.3160477","volume":"9","author":"S Xiao","year":"2023","unstructured":"Xiao S, Wang S, Guo W (2023) SGAE: stacked graph autoencoder for deep clustering. IEEE Trans Big Data 9(1):254\u2013266","journal-title":"IEEE Trans Big Data"},{"key":"1435_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108977","volume":"133","author":"A Agibetov","year":"2023","unstructured":"Agibetov A (2023) Neural graph embeddings as explicit low-rank matrix factorization for link prediction. Pattern Recogn 133:108977","journal-title":"Pattern Recogn"},{"issue":"1","key":"1435_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2012.02.006","volume":"519","author":"L L\u00fc","year":"2012","unstructured":"L\u00fc L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519(1):1\u201349","journal-title":"Phys Rep"},{"key":"1435_CR4","first-page":"1024","volume":"30","author":"W Hamilton","year":"2017","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Neural Inf Process Syst 30:1024\u20131034","journal-title":"Neural Inf Process Syst"},{"key":"1435_CR5","doi-asserted-by":"crossref","unstructured":"Brin S, Page L (1998) The anatomy of a large scale hypertextual web search engine. In: WWW Conf","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"1435_CR6","unstructured":"Page L, Brin S, Motwani R, Winograd T (1998) The pagerank citation ranking: bringing order to the web. In: WWW Conf., pp 161\u2013172"},{"key":"1435_CR7","unstructured":"Zhou D, Bousquet O, Lal T, Weston J, Sch\u00f6lkopf B (2004) Learning with local and global consistency. In NIPS, pp. 321\u2013328"},{"key":"1435_CR8","unstructured":"Chen M, Yang Q, Tang X (2007) Directed graph embedding. In: Proceedings of IJCAI. pp 2707\u20132712"},{"issue":"12","key":"1435_CR9","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1093\/bioinformatics\/btu269","volume":"30","author":"N Natarajan","year":"2014","unstructured":"Natarajan N, Dhillon IS (2014) Inductive matrix completion for predicting gene-disease associations. Bioinformatics 30(12):60\u201368","journal-title":"Bioinformatics"},{"key":"1435_CR10","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: A library for large linear classification. JMLR 9:1871\u20131874","journal-title":"JMLR"},{"key":"1435_CR11","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Process Syst 14(6):585\u201359","DOI":"10.7551\/mitpress\/1120.003.0080"},{"key":"1435_CR12","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: 1st international conference on learning representations, Scottsdale, Arizona, USA"},{"key":"1435_CR13","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. KDD pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"1435_CR14","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. KDD. pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"1435_CR15","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: WWW conference. pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"1435_CR16","doi-asserted-by":"crossref","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: ACM SIGKDD conference. pp 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"1435_CR17","unstructured":"Tu C, Zhang W, Liu Z, Sun M (2016) Max-margin deepwalk: discriminative learning of network representation. In: IJCAI conference. AAAI Press, pp 3889\u20133895"},{"key":"1435_CR18","unstructured":"Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information. In: IJCAI conference. AAAI Press, pp 2111\u20132117"},{"key":"1435_CR19","unstructured":"Sun X, Guo J, Ding X, Liu T (2016) A general framework for content-enhanced network representation learning. arXiv preprint arXiv:1610.02906"},{"key":"1435_CR20","unstructured":"Pan S, Wu J, Zhu X, Zhang C, Wang Y (2016) Tri-party deep network representation. In: IJCAI conference. AAAI Press, pp 1895\u20131901"},{"issue":"1","key":"1435_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00026-005-0237-z","volume":"9","author":"F Chung","year":"2005","unstructured":"Chung F (2005) Laplacians and the Cheeger inequality for directed graphs. Ann Combin 9(1):1\u201319","journal-title":"Ann Combin"},{"key":"1435_CR22","doi-asserted-by":"crossref","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: ACM SIGKDD. pp 1105\u20131114","DOI":"10.1145\/2939672.2939751"},{"key":"1435_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/BF02289026","volume":"18","author":"L Katz","year":"1953","unstructured":"Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39\u201343","journal-title":"Psychometrika"},{"key":"1435_CR24","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. Proceedings of IEEE CSCCVPR 731\u2013737"},{"key":"1435_CR25","doi-asserted-by":"crossref","unstructured":"Satuluri V, Parthasarathy, S (2011) Symmetrizations for clustering directed graphs. EDBT\/ICDT Workshops. pp 343\u2013354","DOI":"10.1145\/1951365.1951407"},{"key":"1435_CR26","unstructured":"Yu HF, Jain P, Kar P, Dhillon AIS. (2013). Large-scale Multi-label Learning with Missing Labels. International Conference on Machine Learning"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01435-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01435-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01435-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T17:23:38Z","timestamp":1721237018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01435-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1435"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01435-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"4 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}