{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:26:36Z","timestamp":1743063996294,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031083327"},{"type":"electronic","value":"9783031083334"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08333-4_21","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T15:52:13Z","timestamp":1655394733000},"page":"257-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Double-View Link Prediction Within the\u00a0Dynamic Interaction Network"],"prefix":"10.1007","author":[{"given":"Jianye","family":"Pang","sequence":"first","affiliation":[]},{"given":"Wei","family":"Ke","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"21_CR1","unstructured":"Aloosh, A., Li, J.: Direct evidence of bitcoin wash trading. SSRN 3362153 (2019)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhao, L., Bian, J., Xing, C., Liu, T.Y.: Investment behaviors can tell what inside: exploring stock intrinsic properties for stock trend prediction. In: ACM SIGKDD (2019)","DOI":"10.1145\/3292500.3330663"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Cong, L.W., Li, X., Tang, K., Yang, Y.: Crypto wash trading. SSRN 3530220 (2020)","DOI":"10.2139\/ssrn.3530220"},{"issue":"1","key":"21_CR4","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jfds.2016.03.002","volume":"2","author":"R Dash","year":"2016","unstructured":"Dash, R., Dash, P.K.: A hybrid stock trading framework integrating technical analysis with machine learning techniques. J. Financ. Data Sci. 2(1), 42\u201357 (2016)","journal-title":"J. Financ. Data Sci."},{"key":"21_CR5","unstructured":"Goyal, P., Kamra, N., He, X., Liu, Y.: DynGEM: deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018)"},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.ribaf.2016.09.011","volume":"39","author":"A Habib","year":"2017","unstructured":"Habib, A., Hasan, M.M.: Business strategy, overvalued equities, and stock price crash risk. Res. Int. Bus. Fin. 39, 389\u2013405 (2017)","journal-title":"Res. Int. Bus. Fin."},{"issue":"70","key":"21_CR7","first-page":"1","volume":"21","author":"SM Kazemi","year":"2020","unstructured":"Kazemi, S.M., et al.: Representation learning for dynamic graphs: a survey. J. Mach. Learn. Res. 21(70), 1\u201373 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: ACM SIGKDD (2019)","DOI":"10.1145\/3292500.3330895"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Li, C., Song, D., Tao, D.: Multi-task recurrent neural networks and higher-order Markov random fields for stock price movement prediction: multi-task RNN and higher-order MRFs for stock price classification. In: ACM SIGKDD (2019)","DOI":"10.1145\/3292500.3330983"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, D., Zhao, L., Bian, J., Qin, T., Liu, T.Y.: Individualized indicator for all: stock-wise technical indicator optimization with stock embedding. In: ACM SIGKDD, pp. 894\u2013902 (2019)","DOI":"10.1145\/3292500.3330833"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Lu, Y., Wang, X., Shi, C., Yu, P.S., Ye, Y.: Temporal network embedding with micro-and macro-dynamics. In: CIKM (2019)","DOI":"10.1145\/3357384.3357943"},{"issue":"11","key":"21_CR12","doi-asserted-by":"publisher","first-page":"3320","DOI":"10.1257\/aer.20140759","volume":"107","author":"S Malamud","year":"2017","unstructured":"Malamud, S., Rostek, M.: Decentralized exchange. Am. Econ. Rev. 107(11), 3320\u201362 (2017)","journal-title":"Am. Econ. Rev."},{"key":"21_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107000","volume":"97","author":"F Manessi","year":"2020","unstructured":"Manessi, F., Rozza, A., Manzo, M.: Dynamic graph convolutional networks. Pattern Recogn. 97, 107000 (2020)","journal-title":"Pattern Recogn."},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen, G.H., Lee, J.B., Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S.: Continuous-time dynamic network embeddings. In: Companion Proceedings of the The Web Conference (2018)","DOI":"10.1145\/3184558.3191526"},{"issue":"3","key":"21_CR15","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1002\/isaf.1438","volume":"25","author":"DE O\u2019Leary","year":"2018","unstructured":"O\u2019Leary, D.E.: Open information enterprise transactions: business intelligence and wash and spoof transactions in blockchain and social commerce. Intell. Syst. Account. Financ. Manage. 25(3), 148\u2013158 (2018)","journal-title":"Intell. Syst. Account. Financ. Manage."},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Pareja, A., et al.: EvolveGCN: evolving graph convolutional networks for dynamic graphs. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Qu, L., Zhu, H., Duan, Q., Shi, Y.: Continuous-time link prediction via temporal dependent graph neural network. In: Proceedings of The Web Conference (2020)","DOI":"10.1145\/3366423.3380073"},{"issue":"3","key":"21_CR18","doi-asserted-by":"crossref","first-page":"60","DOI":"10.11648\/j.jim.20190803.11","volume":"8","author":"SRM Sabri","year":"2019","unstructured":"Sabri, S.R.M., Sarsour, W.M.: Modelling on stock investment valuation for long-term strategy. J. Invest. Manage. 8(3), 60\u201366 (2019)","journal-title":"J. Invest. Manage."},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H.: DySAT: deep neural representation learning on dynamic graphs via self-attention networks. In: WSDM (2020)","DOI":"10.1145\/3336191.3371845"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Sch\u00e4r, F.: Decentralized finance: on blockchain-and smart contract-based financial markets. SSRN 3571335 (2020)","DOI":"10.2139\/ssrn.3571335"},{"issue":"1","key":"21_CR21","first-page":"4","volume":"36","author":"DC Shapiro","year":"2018","unstructured":"Shapiro, D.C.: Taxation and regulation in decentralized exchanges. J. Tax. Invest. 36(1), 4 (2018)","journal-title":"J. Tax. Invest."},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Skarding, J., Gabrys, B., Musial, K.: Foundations and modelling of dynamic networks using dynamic graph neural networks: a survey. arXiv preprint arXiv:2005.07496 (2020)","DOI":"10.1109\/ACCESS.2021.3082932"},{"key":"21_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2018.12.032","volume":"32","author":"X Sun","year":"2020","unstructured":"Sun, X., Liu, M., Sima, Z.: A novel cryptocurrency price trend forecasting model based on LightGBM. Financ. Res. Lett. 32, 101084 (2020)","journal-title":"Financ. Res. Lett."},{"key":"21_CR24","unstructured":"Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: deep temporal reasoning for dynamic knowledge graphs. In: ICML (2017)"},{"key":"21_CR25","unstructured":"Trivedi, R., Farajtabar, M., Biswal, P., Zha, H.: DyREP: learning representations over dynamic graphs. In: ICLR (2019)"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, L., Aggarwal, C., Qi, G.J.: Stock price prediction via discovering multi-frequency trading patterns. In: ACM SIGKDD (2017)","DOI":"10.1145\/3097983.3098117"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Zuo, Y., Liu, G., Lin, H., Guo, J., Hu, X., Wu, J.: Embedding temporal network via neighborhood formation. In: ACM SIGKDD (2018)","DOI":"10.1145\/3219819.3220054"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08333-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T16:17:52Z","timestamp":1675873072000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08333-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031083327","9783031083334"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08333-4_21","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}