{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:22:33Z","timestamp":1770754953469,"version":"3.50.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030922375","type":"print"},{"value":"9783030922382","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-92238-2_5","type":"book-chapter","created":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T22:02:35Z","timestamp":1638655355000},"page":"52-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Learning to\u00a0Coordinate via\u00a0Multiple Graph Neural Networks"],"prefix":"10.1007","author":[{"given":"Zhiwei","family":"Xu","sequence":"first","affiliation":[]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yunpeng","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Dapeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guoliang","family":"Fan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"5_CR1","unstructured":"B\u00f6hmer, W., Kurin, V., Whiteson, S.: Deep coordination graphs. arXiv arXiv:1910.00091 (2020)"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603\u2013619 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR3","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS (2016)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Foerster, J.N., Farquhar, G., Afouras, T., Nardelli, N., Whiteson, S.: Counterfactual multi-agent policy gradients. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11794"},{"key":"5_CR5","unstructured":"Ha, D., Dai, A.M., Le, Q.V.: Hypernetworks. arXiv arXiv:1609.09106 (2017)"},{"key":"5_CR6","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS (2017)"},{"key":"5_CR7","unstructured":"Hausknecht, M.J., Stone, P.: Deep recurrent Q-learning for partially observable MDPs. In: AAAI Fall Symposia (2015)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR9","unstructured":"Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: NIPS (2017)"},{"key":"5_CR10","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Oliehoek, F.A., Amato, C.: A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-28929-8","DOI":"10.1007\/978-3-319-28929-8"},{"key":"5_CR12","unstructured":"Peng, P., et al.: Multiagent Bidirectionally-Coordinated Nets: emergence of human-level coordination in learning to play StarCraft combat games. arXiv: Artificial Intelligence (2017)"},{"key":"5_CR13","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 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"5_CR14","unstructured":"Rashid, T., Samvelyan, M., Witt, C.S., Farquhar, G., Foerster, J.N., Whiteson, S.: QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning. arXiv arXiv:1803.11485 (2018)"},{"key":"5_CR15","unstructured":"Samvelyan, M., et al.: The StarCraft multi-agent challenge. arXiv arXiv:1902.04043 (2019)"},{"key":"5_CR16","unstructured":"Son, K., Kim, D., Kang, W., Hostallero, D., Yi, Y.: QTRAN: learning to factorize with transformation for cooperative multi-agent reinforcement learning. arXiv arXiv:1905.05408 (2019)"},{"key":"5_CR17","unstructured":"Sukhbaatar, S., Szlam, A., Fergus, R.: Learning multiagent communication with backpropagation. In: NIPS (2016)"},{"key":"5_CR18","unstructured":"Sunehag, P., et al.: Value-decomposition networks for cooperative multi-agent learning. arXiv arXiv:1706.05296 (2018)"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/TNN.2004.842673","volume":"16","author":"R Sutton","year":"2005","unstructured":"Sutton, R., Barto, A.: Reinforcement learning: an introduction. IEEE Trans. Neural Netw. 16, 285\u2013286 (2005)","journal-title":"IEEE Trans. Neural Netw."},{"key":"5_CR20","doi-asserted-by":"publisher","first-page":"e0172395","DOI":"10.1371\/journal.pone.0172395","volume":"12","author":"A Tampuu","year":"2017","unstructured":"Tampuu, A., et al.: Multiagent cooperation and competition with deep reinforcement learning. PLOS ONE 12, e0172395 (2017)","journal-title":"PLOS ONE"},{"key":"5_CR21","unstructured":"Thekumparampil, K.K., Wang, C., Oh, S., Li, L.: Attention-based graph neural network for semi-supervised learning. arXiv arXiv:1803.03735 (2018)"},{"key":"5_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv arXiv:1706.03762 (2017)"},{"key":"5_CR23","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. arXiv arXiv:1710.10903 (2018)"},{"key":"5_CR24","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv arXiv:1810.00826 (2019)"},{"key":"5_CR25","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: ICML (2018)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92238-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:49:33Z","timestamp":1710355773000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92238-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030922375","9783030922382"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92238-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.57","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}