{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T19:40:27Z","timestamp":1726083627932},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030474355"},{"type":"electronic","value":"9783030474362"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-47436-2_42","type":"book-chapter","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T03:02:47Z","timestamp":1588906967000},"page":"555-567","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Role Equivalence Attention for Label Propagation in Graph Neural Networks"],"prefix":"10.1007","author":[{"given":"Hogun","family":"Park","sequence":"first","affiliation":[]},{"given":"Jennifer","family":"Neville","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"key":"42_CR1","unstructured":"Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. In: Proceedings of UAI (2019)"},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of CIKM (2015)","DOI":"10.1145\/2806416.2806512"},{"key":"42_CR3","unstructured":"Chen, J., Ma, T., Xiao, C.: FastGCN: fast learning with graph convolutional networks via importance sampling. In: Proceedings of ICLR (2018)"},{"issue":"6","key":"42_CR4","doi-asserted-by":"publisher","first-page":"066111","DOI":"10.1103\/PhysRevE.70.066111","volume":"70","author":"A Clauset","year":"2004","unstructured":"Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)","journal-title":"Phys. Rev. E"},{"issue":"1","key":"42_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1080\/0022250X.1994.9990134","volume":"19","author":"MG Everett","year":"1994","unstructured":"Everett, M.G., Borgatti, S.P.: Regular equivalence: general theory. J. Math. Soc. 19(1), 29\u201352 (1994)","journal-title":"J. Math. Soc."},{"issue":"3\u20134","key":"42_CR6","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1080\/0022250X.1990.9990067","volume":"15","author":"MG Everett","year":"1990","unstructured":"Everett, M.G., Boyd, J.P., Borgatti, S.P.: Ego-centered and local roles: a graph theoretic approach. J. Math. Soc. 15(3\u20134), 163\u2013172 (1990)","journal-title":"J. Math. Soc."},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: Proceedings of SIGKDD, pp. 256\u2013264. ACM (2008)","DOI":"10.1145\/1401890.1401925"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of SIGKDD (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"42_CR9","unstructured":"Hoshen, Y.: Vain: attentional multi-agent predictive modeling. In: Proceedings of NeurIPS, pp. 2701\u20132711 (2017)"},{"key":"42_CR10","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of ICLR (2017)"},{"issue":"1","key":"42_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1080\/0022250X.1971.9989788","volume":"1","author":"F Lorrain","year":"1971","unstructured":"Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Soc. 1(1), 49\u201380 (1971)","journal-title":"J. Math. Soc."},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Pfeiffer, III, J.J., Neville, J., Bennett, P.N.: Overcoming relational learning biases to accurately predict preferences in large scale networks. In: Proceedings of WWW (2015)","DOI":"10.1145\/2736277.2741668"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: struc2vec: learning node representations from structural identity. In: Proceedings of SIGKDD (2017)","DOI":"10.1145\/3097983.3098061"},{"issue":"7","key":"42_CR14","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1109\/TSP.2013.2238935","volume":"61","author":"A Sandryhaila","year":"2013","unstructured":"Sandryhaila, A., Moura, J.M.: Discrete signal processing on graphs. IEEE Trans. Signal Process. 61(7), 1644\u20131656 (2013)","journal-title":"IEEE Trans. Signal Process."},{"issue":"3","key":"42_CR15","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)","journal-title":"AI Mag."},{"key":"42_CR16","unstructured":"Teixeira, L., Jalaian, B., Ribeiro, B.: Are graph neural networks miscalibrated? In: Proceedings of Learning and Reasoning with Graph-Structured Representations Workshop in ICML 2019 (2019)"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Tsitsulin, A., Mottin, D., Karras, P., M\u00fcller, E.: Verse: versatile graph embeddings from similarity measures. In: Proceedings of WWW (2018)","DOI":"10.1145\/3178876.3186120"},{"key":"42_CR18","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of ICLR (2018)"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Yang, C., Sun, M., Liu, Z., Tu, C.: Fast network embedding enhancement via high order proximity approximation. In: Proceedings of IJCAI (2017)","DOI":"10.24963\/ijcai.2017\/544"},{"key":"42_CR20","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: Proceedings of ICML (2016)"},{"issue":"4","key":"42_CR21","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1086\/jar.33.4.3629752","volume":"33","author":"WW Zachary","year":"1977","unstructured":"Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452\u2013473 (1977)","journal-title":"J. Anthropol. Res."},{"key":"42_CR22","unstructured":"Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: Proceedings of NIPS (2004)"},{"key":"42_CR23","unstructured":"Zhou, Z., Li, X.: Graph convolution: a high-order and adaptive approach. In: Proceedings of AAAI (2017)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-47436-2_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T05:08:45Z","timestamp":1588914525000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-47436-2_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030474355","9783030474362"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-47436-2_42","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":"6 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"11 May 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2020.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"628","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":"135","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":"0","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":"3-4","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":"6-8","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}