{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:28:49Z","timestamp":1743128929802,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030620042"},{"type":"electronic","value":"9783030620059"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62005-9_1","type":"book-chapter","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T11:02:34Z","timestamp":1602932554000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Higher-Order Graph Convolutional Embedding for Temporal Networks"],"prefix":"10.1007","author":[{"given":"Xian","family":"Mo","sequence":"first","affiliation":[]},{"given":"Jun","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Zhiming","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"key":"1_CR1","unstructured":"Abu-El-Haija, S., et al.: MixHop: higher-order graph convolution architectures via sparsified neighborhood mixing. In: Proceedings of the 36th International Conference on Machine Learning, pp. 21\u201329 (2019)"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., Li, J.: Exploiting structural and temporal evolution in dynamic link prediction. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 427\u2013436. ACM (2018)","DOI":"10.1145\/3269206.3271740"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of the 8th Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103\u2013111 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"issue":"5","key":"1_CR4","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833\u2013852 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Cui, W., et al.: Let it flow: a static method for exploring dynamic graphs. In: Proceedings of the 2014 IEEE Pacific Visualization Symposium, pp. 121\u2013128. IEEE (2014)","DOI":"10.1109\/PacificVis.2014.48"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"104816","DOI":"10.1016\/j.knosys.2019.06.024","volume":"187","author":"P Goyal","year":"2020","unstructured":"Goyal, P., Chhetri, S.R., Canedo, A.: dyngraph2vec: capturing network dynamics using dynamic graph representation learning. Knowl.-Based Syst. 187, 104816 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"1_CR7","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"1_CR8","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: Proceedings of the 2018 International Conference on Learning Representations, pp. 1\u201316. IEEE (2018)"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Pang, J., Zhang, Y.: DeepCity: a feature learning framework for mining location check-ins. In: Proceedings of the 11th International AAAI Conference on Web and Social Media, pp. 652\u2013655 (2017)","DOI":"10.1609\/icwsm.v11i1.14906"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"1_CR11","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: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 519\u2013527. ACM (2020)","DOI":"10.1145\/3336191.3371845"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Singer, U., Guy, I., Radinsky, K.: Node embedding over temporal graphs. In: Proceedings of the 2019 International Joint Conference on Artificial Intelligence, pp. 4605\u20134612. Morgan Kaufmann (2019)","DOI":"10.24963\/ijcai.2019\/640"},{"issue":"5500","key":"1_CR13","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319\u20132323 (2000)","journal-title":"Science"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3634\u20133640. Morgan Kaufmann (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Yu, W., Cheng, W., Aggarwal, C.C., Zhang, K., Chen, H., Wang, W.: NetWalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2672\u20132681. ACM (2018)","DOI":"10.1145\/3219819.3220024"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Yu, W., Wei, C., Aggarwal, C.C., Chen, H., Wei, W.: Link prediction with spatial and temporal consistency in dynamic networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 3343\u20133349 (2017)","DOI":"10.24963\/ijcai.2017\/467"},{"issue":"10","key":"1_CR17","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1109\/TKDE.2016.2591009","volume":"28","author":"L Zhu","year":"2016","unstructured":"Zhu, L., Dong, G., Yin, J., Steeg, G.V., Galstyan, A.: Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Trans. Knowl. Data Eng. 28(10), 2765\u20132777 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62005-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:03:28Z","timestamp":1669230208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62005-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030620042","9783030620059"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62005-9_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasp.cs.vu.nl\/WISE2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}