{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T09:10:13Z","timestamp":1686906613891},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"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":"crossref","award":["62272405"],"award-info":[{"award-number":["62272405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"School and Locality Integration Development Project of Yantai City","award":["JS22JY02"],"award-info":[{"award-number":["JS22JY02"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Provincial","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["2022XDRH023"],"award-info":[{"award-number":["2022XDRH023"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Graph representation learning has made significant strides in various fields, including sociology and biology, in recent years. However, the majority of research has focused on static graphs, neglecting the temporality and continuity of edges in dynamic graphs. Furthermore, dynamic data are vulnerable to various security threats, such as data privacy breaches and confidentiality attacks. To tackle this issue, the present paper proposes a data security detection method based on a continuous-time graph embedding framework (CTDGE). The framework models temporal dependencies and embeds data using a graph representation learning method. A machine learning algorithm is then employed to classify and predict the embedded data to detect if it is secure or not. Experimental results show that this method performs well in data security detection, surpassing several dynamic graph embedding methods by 5% in terms of AUC metrics. Furthermore, the proposed framework outperforms other dynamic baseline methods in the node classification task of large-scale graphs containing 4321477 temporal information edges, resulting in a 10% improvement in the F1 score metric. The framework is also robust and scalable for application in various data security domains. This work is important for promoting the use of continuous-time graph embedding framework in the field of data security.<\/jats:p>","DOI":"10.1186\/s13677-023-00460-4","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T04:02:03Z","timestamp":1686801723000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A large-scale data security detection method based on continuous time graph embedding framework"],"prefix":"10.1186","volume":"12","author":[{"given":"Zhaowei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Weishuai","family":"Che","sequence":"additional","affiliation":[]},{"given":"Shenqiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jindong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"issue":"1","key":"460_CR1","doi-asserted-by":"publisher","first-page":"102385","DOI":"10.1016\/j.ipm.2020.102385","volume":"58","author":"M Alassad","year":"2021","unstructured":"Alassad M, Spann B, Agarwal N (2021) Combining advanced computational social science and graph theoretic techniques to reveal adversarial information operations. Inf Process Manag 58(1):102385","journal-title":"Inf Process Manag"},{"issue":"3","key":"460_CR2","doi-asserted-by":"publisher","first-page":"102188","DOI":"10.1016\/j.ipm.2019.102188","volume":"57","author":"MJ Hussain","year":"2020","unstructured":"Hussain MJ, Wasti SH, Huang G, Wei L, Jiang Y, Tang Y (2020) An approach for measuring semantic similarity between wikipedia concepts using multiple inheritances. Inf Process Manag 57(3):102188","journal-title":"Inf Process Manag"},{"issue":"2","key":"460_CR3","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","volume":"17","author":"P Gainza","year":"2020","unstructured":"Gainza P, Sverrisson F, Monti F, Rodola E, Boscaini D, Bronstein M, Correia B (2020) Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat Methods 17(2):184\u2013192","journal-title":"Nat Methods"},{"issue":"3","key":"460_CR4","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1109\/TCBB.2020.2994780","volume":"18","author":"X Zhou","year":"2021","unstructured":"Zhou X, Li Y, Liang W (2021) Cnn-rnn based intelligent recommendation for online medical pre-diagnosis support. IEEE\/ACM Trans Comput Biol Bioinforma 18(3):912\u2013921. https:\/\/doi.org\/10.1109\/TCBB.2020.2994780","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"key":"460_CR5","doi-asserted-by":"crossref","unstructured":"Gong W, Zhang X, Chen Y, He Q, Beheshti A, Xu X, Yan C, Qi L (2022) Dawar: Diversity-aware web apis recommendation for mashup creation based on correlation graph. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, pp 395\u2013404","DOI":"10.1145\/3477495.3531962"},{"key":"460_CR6","first-page":"969","volume":"2018","author":"GH Nguyen","year":"2018","unstructured":"Nguyen GH, Lee JB, Rossi RA, Ahmed NK, Koh E, Kim S (2018) Continuous-time dynamic network embeddings. Companion Proceedings of the The Web Conference 2018:969\u2013976","journal-title":"Companion Proceedings of the The Web Conference"},{"key":"460_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-020-00257-3","volume":"5","author":"F Heidari","year":"2020","unstructured":"Heidari F, Papagelis M (2020) Evolving network representation learning based on random walks. Appl Netw Sci 5:1\u201338","journal-title":"Appl Netw Sci"},{"key":"460_CR8","doi-asserted-by":"crossref","unstructured":"Qi L, Chi X, Zhou X, Liu Q, Dai F, Xu X, Zhang X (2022) Privacy-aware data fusion and prediction for smart city services in edge computing environment. In: 2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), IEEE, pp 9\u201316","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00043"},{"key":"460_CR9","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s10618-019-00626-2","volume":"33","author":"JP Mei","year":"2019","unstructured":"Mei JP, Lv H, Yang L, Li Y (2019) Clustering for heterogeneous information networks with extended star-structure. Data Min Knowl Disc 33:1059\u20131087","journal-title":"Data Min Knowl Disc"},{"key":"460_CR10","doi-asserted-by":"crossref","unstructured":"Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, pp 5363\u20135370","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"460_CR11","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1017\/ATSIP.2020.13","volume":"9","author":"F Chen","year":"2020","unstructured":"Chen F, Wang YC, Wang B, Kuo CCJ (2020) Graph representation learning: a survey. APSIPA Trans Signal Inf Process 9:15","journal-title":"APSIPA Trans Signal Inf Process"},{"key":"460_CR12","unstructured":"Trivedi R, Farajtabar M, Biswal P, Zha H (2019) Dyrep: Learning representations over dynamic graphs. In: International conference on learning representations. [Online]."},{"key":"460_CR13","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"460_CR14","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"issue":"12","key":"460_CR15","doi-asserted-by":"publisher","first-page":"9310","DOI":"10.1109\/JIOT.2021.3130434","volume":"9","author":"X Zhou","year":"2021","unstructured":"Zhou X, Liang W, Li W, Yan K, Shimizu S, Kevin I, Wang K (2021) Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system. IEEE Internet Things J 9(12):9310\u20139319","journal-title":"IEEE Internet Things J"},{"issue":"16","key":"460_CR16","doi-asserted-by":"publisher","first-page":"12588","DOI":"10.1109\/JIOT.2021.3077449","volume":"8","author":"X Zhou","year":"2021","unstructured":"Zhou X, Xu X, Liang W, Zeng Z, Yan Z (2021) Deep-learning-enhanced multitarget detection for end-edge-cloud surveillance in smart IoT. IEEE Internet Things J 8(16):12588\u201312596","journal-title":"IEEE Internet Things J"},{"key":"460_CR17","doi-asserted-by":"crossref","unstructured":"Wang W, Wang Y, Duan P, Liu T, Tong X, Cai Z (2022) A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing. IEEE Trans Mob Comput, pp 1\u201318","DOI":"10.1109\/TMC.2022.3187047"},{"issue":"6","key":"460_CR18","doi-asserted-by":"publisher","first-page":"4187","DOI":"10.1109\/TII.2019.2936869","volume":"16","author":"X Xu","year":"2019","unstructured":"Xu X, Zhang X, Gao H, Xue Y, Qi L, Dou W (2019) Become: Blockchain-enabled computation offloading for IoT in mobile edge computing. IEEE Trans Ind Inform 16(6):4187\u20134195","journal-title":"IEEE Trans Ind Inform"},{"issue":"11","key":"460_CR19","doi-asserted-by":"publisher","first-page":"4593","DOI":"10.1109\/TFUZZ.2022.3158000","volume":"30","author":"X Xu","year":"2022","unstructured":"Xu X, Jiang Q, Zhang P, Cao X, Khosravi MR, Alex LT, Qi L, Dou W (2022) Game theory for distributed iov task offloading with fuzzy neural network in edge computing. IEEE Trans Fuzzy Syst 30(11):4593\u20134604","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"8","key":"460_CR20","doi-asserted-by":"publisher","first-page":"5790","DOI":"10.1109\/TII.2020.3047675","volume":"17","author":"X Zhou","year":"2020","unstructured":"Zhou X, Liang W, Shimizu S, Ma J, Jin Q (2020) Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems. IEEE Trans Ind Inform 17(8):5790\u20135798","journal-title":"IEEE Trans Ind Inform"},{"issue":"8","key":"460_CR21","doi-asserted-by":"publisher","first-page":"5087","DOI":"10.1109\/TII.2021.3116085","volume":"18","author":"W Liang","year":"2021","unstructured":"Liang W, Hu Y, Zhou X, Pan Y, Kevin I, Wang K (2021) Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial IoT. IEEE Trans Ind Inform 18(8):5087\u20135095","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"460_CR22","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TII.2021.3076811","volume":"19","author":"Z Lu","year":"2021","unstructured":"Lu Z, Wang Y, Tong X, Mu C, Chen Y, Li Y (2021) Data-driven many-objective crowd worker selection for mobile crowdsourcing in industrial IoT. IEEE Trans Ind Inform 19(1):531\u2013540","journal-title":"IEEE Trans Ind Inform"},{"key":"460_CR23","doi-asserted-by":"crossref","unstructured":"Makarov I, Kiselev D, Nikitinsky N, Subelj L (2021) Survey on graph embeddings and their applications to machine learning problems on graphs. PeerJ Comput Sci 7","DOI":"10.7717\/peerj-cs.357"},{"issue":"1","key":"460_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3483595","volume":"55","author":"CD Barros","year":"2021","unstructured":"Barros CD, Mendon\u00e7a MR, Vieira AB, Ziviani A (2021) A survey on embedding dynamic graphs. ACM Comput Surv (CSUR) 55(1):1\u201337","journal-title":"ACM Comput Surv (CSUR)"},{"key":"460_CR25","doi-asserted-by":"crossref","unstructured":"Wang Y, Liu Z, Xu J, Yan W (2022) Heterogeneous network representation learning approach for ethereum identity identification. IEEE Trans Comput Soc Syst, pp 890\u2013899","DOI":"10.1109\/TCSS.2022.3164719"},{"key":"460_CR26","doi-asserted-by":"crossref","unstructured":"Liu Z, Yang D, Wang S, Su H (2022) Adaptive multi-channel bayesian graph attention network for iot transaction security. Digit Commun Netw, pp 1\u201320","DOI":"10.1016\/j.dcan.2022.11.018"},{"key":"460_CR27","doi-asserted-by":"publisher","first-page":"110040","DOI":"10.1016\/j.asoc.2023.110040","volume":"135","author":"Z Liu","year":"2023","unstructured":"Liu Z, Yang D, Wang Y, Lu M, Li R (2023) Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks. Appl Soft Comput 135:110040","journal-title":"Appl Soft Comput"},{"issue":"4","key":"460_CR28","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.1007\/s11063-020-10404-7","volume":"54","author":"H Zhang","year":"2022","unstructured":"Zhang H, Lu G, Zhan M, Zhang B (2022) Semi-supervised classification of graph convolutional networks with laplacian rank constraints. Neural Process Lett 54(4):2645\u20132656","journal-title":"Neural Process Lett"},{"key":"460_CR29","unstructured":"You J, Ying R, Ren X, Hamilton W, Leskovec J (2018) Graphrnn: Generating realistic graphs with deep auto-regressive models. In: International conference on machine learning, PMLR, pp 5708\u20135717"},{"key":"460_CR30","first-page":"20","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. Stat 1050:20","journal-title":"Stat"},{"issue":"5","key":"460_CR31","doi-asserted-by":"publisher","first-page":"102680","DOI":"10.1016\/j.ipm.2021.102680","volume":"58","author":"G Jin","year":"2021","unstructured":"Jin G, Liu C, Chen X (2021) Adversarial network integrating dual attention and sparse representation for semi-supervised semantic segmentation. Inf Process Manag 58(5):102680","journal-title":"Inf Process Manag"},{"key":"460_CR32","first-page":"1","volume":"32","author":"S Yun","year":"2019","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Adv Neural Inf Process Syst 32:1\u201311","journal-title":"Adv Neural Inf Process Syst"},{"key":"460_CR33","doi-asserted-by":"crossref","unstructured":"Li R, Liu Z, Ma Y, Yang D, Sun S (2022) Internet financial fraud detection based on graph learning. IEEE Trans Comput Soc Syst, 1394\u20131401","DOI":"10.1109\/TCSS.2022.3189368"},{"issue":"104","key":"460_CR34","first-page":"816","volume":"187","author":"P Goyal","year":"2020","unstructured":"Goyal P, Chhetri SR, Canedo A (2020) dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowl-Based Syst 187(104):816","journal-title":"Knowl-Based Syst"},{"key":"460_CR35","doi-asserted-by":"crossref","unstructured":"Hisano R (2018) Semi-supervised graph embedding approach to dynamic link prediction. In: International Workshop on Complex Networks, Springer, pp 109\u2013121","DOI":"10.1007\/978-3-319-73198-8_10"},{"key":"460_CR36","doi-asserted-by":"crossref","unstructured":"Wu Z, Zhan M, Zhang H, Luo Q, Tang K (2022) Mtgcn: A multi-task approach for node classification and link prediction in graph data. Inf Process Manag 59(3):102902","DOI":"10.1016\/j.ipm.2022.102902"},{"key":"460_CR37","doi-asserted-by":"crossref","unstructured":"Haddad M, Bothorel C, Lenca P, Bedart D (2019) Temporalnode2vec: Temporal node embedding in temporal networks. In: International Conference on Complex Networks and Their Applications, Springer, pp 891\u2013902","DOI":"10.1007\/978-3-030-36687-2_74"},{"key":"460_CR38","doi-asserted-by":"crossref","unstructured":"Hu L, Li C, Shi C, Yang C, Shao C (2020) Graph neural news recommendation with long-term and short-term interest modeling. Inf Process Manag 57(2):102142","DOI":"10.1016\/j.ipm.2019.102142"},{"issue":"3","key":"460_CR39","first-page":"1","volume":"16","author":"L Chen","year":"2021","unstructured":"Chen L, Tang X, Chen W, Qian Y, Li Y, Zhang Y (2021) Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation. ACM Trans Knowl Disc Data (TKDD) 16(3):1\u201318","journal-title":"ACM Trans Knowl Disc Data (TKDD)"},{"key":"460_CR40","first-page":"1629","volume":"2021","author":"Z Liu","year":"2021","unstructured":"Liu Z, Huang C, Yu Y, Dong J (2021) Motif-preserving dynamic attributed network embedding. Proc Web Conference 2021:1629\u20131638","journal-title":"Proc Web Conference"},{"key":"460_CR41","doi-asserted-by":"crossref","unstructured":"Cui Z, Li Z, Wu S, Zhang X, Liu Q, Wang L, Ai M (2022) Dygcn: Efficient dynamic graph embedding with graph convolutional network. IEEE Trans Neural Netw Learn Syst, pp 1\u201312","DOI":"10.1109\/TNNLS.2022.3185527"},{"key":"460_CR42","doi-asserted-by":"crossref","unstructured":"Goel R, Kazemi SM, Brubaker M, Poupart P (2020) Diachronic embedding for temporal knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, pp 3988\u20133995","DOI":"10.1609\/aaai.v34i04.5815"},{"issue":"1","key":"460_CR43","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1109\/TII.2022.3200067","volume":"19","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wu H, Rezaee K, Khosravi MR, Khalaf OI, Khan AA, Ramesh D, Qi L (2022) Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises. IEEE Trans Ind Inf 19(1):635\u2013643","journal-title":"IEEE Trans Ind Inf"},{"key":"460_CR44","doi-asserted-by":"crossref","unstructured":"Huang S, Bao Z, Li G, Zhou Y, Culpepper JS (2020) Temporal network representation learning via historical neighborhoods aggregation. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), IEEE, pp 1117\u20131128","DOI":"10.1109\/ICDE48307.2020.00101"},{"key":"460_CR45","doi-asserted-by":"publisher","first-page":"858","DOI":"10.7717\/peerj-cs.858","volume":"8","author":"I Makarov","year":"2022","unstructured":"Makarov I, Savchenko A, Korovko A, Sherstyuk L, Severin N, Kiselev D, Mikheev A, Babaev D (2022) Temporal network embedding framework with causal anonymous walks representations. PeerJ Comput Sci 8:858","journal-title":"PeerJ Comput Sci"},{"key":"460_CR46","first-page":"3026","volume":"2020","author":"L Qu","year":"2020","unstructured":"Qu L, Zhu H, Duan Q, Shi Y (2020) Continuous-time link prediction via temporal dependent graph neural network. Proceedings of The Web Conference 2020:3026\u20133032","journal-title":"Proceedings of The Web Conference"},{"key":"460_CR47","first-page":"1639","volume":"2021","author":"H Chen","year":"2021","unstructured":"Chen H, Xiong Y, Zhu Y, Yu PS (2021) Highly liquid temporal interaction graph embeddings. Proceedings of the Web Conference 2021:1639\u20131648","journal-title":"Proceedings of the Web Conference"},{"key":"460_CR48","doi-asserted-by":"crossref","unstructured":"Ma Y, Guo Z, Ren Z, Tang J, Yin D (2020) Streaming graph neural networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, pp 719\u2013728","DOI":"10.1145\/3397271.3401092"},{"key":"460_CR49","first-page":"3049","volume":"2020","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Bu J, Ester M, Zhang J, Yao C, Li Z, Wang C (2020) Learning temporal interaction graph embedding via coupled memory networks. Proceedings of the web conference 2020:3049\u20133055","journal-title":"Proceedings of the web conference"},{"key":"460_CR50","first-page":"432","volume-title":"2018 18th IEEE\/ACM International Symposium on Cluster","author":"W Liu","year":"2018","unstructured":"Liu W, Li H, Xie B (2018) Real-time graph partition and embedding of large network. 2018 18th IEEE\/ACM International Symposium on Cluster. Cloud and Grid Computing (CCGRID), IEEE, pp 432\u2013441"},{"key":"460_CR51","doi-asserted-by":"crossref","unstructured":"Goranci G, R\u00e4cke H, Saranurak T, Tan Z (2021) The expander hierarchy and its applications to dynamic graph algorithms. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, pp 2212\u20132228","DOI":"10.1137\/1.9781611976465.132"},{"issue":"3","key":"460_CR52","doi-asserted-by":"publisher","first-page":"102916","DOI":"10.1016\/j.ipm.2022.102916","volume":"59","author":"Y Wan","year":"2022","unstructured":"Wan Y, Yuan C, Zhan M, Chen L (2022) Robust graph learning with graph convolutional network. Inf Process Manag 59(3):102916","journal-title":"Inf Process Manag"},{"issue":"1","key":"460_CR53","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s40747-021-00332-x","volume":"8","author":"A Mohan","year":"2022","unstructured":"Mohan A, Pramod K (2022) Temporal network embedding using graph attention network. Complex Intell Syst 8(1):13\u201327","journal-title":"Complex Intell Syst"},{"key":"460_CR54","doi-asserted-by":"crossref","unstructured":"Zhou L, Yang Y, Ren X, Wu F, Zhuang Y (2018) Dynamic network embedding by modeling triadic closure process. In: Proceedings of the AAAI conference on artificial intelligence. Menlo Park: AAAI Press","DOI":"10.1609\/aaai.v32i1.11257"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00460-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-023-00460-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00460-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T04:07:35Z","timestamp":1686802055000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-023-00460-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,15]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["460"],"URL":"https:\/\/doi.org\/10.1186\/s13677-023-00460-4","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,15]]},"assertion":[{"value":"20 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This material is the authors\u2019 own original work, which has not been previously published elsewhere. The paper is not currently being considered for publication elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"89"}}