{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:28:34Z","timestamp":1771957714841,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030660451","type":"print"},{"value":"9783030660468","type":"electronic"}],"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\/978-3-030-66046-8_4","type":"book-chapter","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T15:02:32Z","timestamp":1609686152000},"page":"39-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Large Scale Graph Analytics for Communities Using Graph Neural Networks"],"prefix":"10.1007","author":[{"given":"Asif Ali","family":"Banka","sequence":"first","affiliation":[]},{"given":"Roohie","family":"Naaz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"4_CR1","unstructured":"Almeida, L.B.: A learning rule for asynchronous perceptrons with feedback in a combinatorial environment. In: Proceedings, 1st First International Conference on Neural Network, Vol. 2, pp. 609\u2013618. IEEE (1987)"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bdr.2016.07.002","volume":"6","author":"S Aridhi","year":"2016","unstructured":"Aridhi, S., Nguifo, E.M.: Big graph mining: frameworks and techniques. Big Data Res. 6, 1\u201310 (2016)","journal-title":"Big Data Res."},{"key":"4_CR3","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs (2013). arXiv preprint: arXiv:1312.6203"},{"issue":"12","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.14778\/2732977.2732980","volume":"7","author":"M Han","year":"2014","unstructured":"Han, M., Daudjee, K., Ammar, K., \u00d6zsu, M.T., Wang, X., Jin, T.: An experimental comparison of pregel-like graph processing systems. Proc. VLDB Endow. 7(12), 1047\u20131058 (2014)","journal-title":"Proc. VLDB Endow."},{"key":"4_CR5","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint: arXiv:1412.6980"},{"key":"4_CR6","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv preprint: arXiv:1609.02907"},{"key":"4_CR7","unstructured":"Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection. http:\/\/snap.stanford.edu\/data (Jun 2014)"},{"issue":"19","key":"4_CR8","doi-asserted-by":"publisher","first-page":"2229","DOI":"10.1103\/PhysRevLett.59.2229","volume":"59","author":"FJ Pineda","year":"1987","unstructured":"Pineda, F.J.: Generalization of back-propagation to recurrent neural networks. Phys. Rev. Lett. 59(19), 2229 (1987)","journal-title":"Phys. Rev. Lett."},{"issue":"2","key":"4_CR9","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1093\/comjnl\/7.2.155","volume":"7","author":"MJ Powell","year":"1964","unstructured":"Powell, M.J.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155\u2013162 (1964)","journal-title":"Comput. J."},{"issue":"1","key":"4_CR10","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains (2012). arXiv preprint: arXiv:1211.0053","DOI":"10.1109\/MSP.2012.2235192"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Yang, J., Leskovec, J.: Community-affiliation graph model for overlapping network community detection. In: 2012 IEEE 12th International Conference on Data Mining, pp. 1170\u20131175. IEEE (2012)","DOI":"10.1109\/ICDM.2012.139"},{"key":"4_CR14","unstructured":"Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: a survey (2018). CoRR abs\/1812.04202: http:\/\/arxiv.org\/abs\/1812.04202"},{"key":"4_CR15","unstructured":"Zhou, Z., Li, X.: Graph convolution: a high-order and adaptive approach (2017). arXiv preprint: arXiv:1706.09916"}],"container-title":["Lecture Notes in Computer Science","Computational Data and Social Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66046-8_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T03:48:57Z","timestamp":1748836137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66046-8_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030660451","9783030660468"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66046-8_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSoNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Data and Social Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dallas, TX","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csonet2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/optnetsci.cise.ufl.edu\/CSoNet\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","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":"20","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":"24% - 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":"3","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)"}}]}}