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Motoda (Eds.), Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19\u201322, 2015, vol. 9077 of Lecture Notes in Computer Science, Springer, 2015, pp. 633\u2013645.","DOI":"10.1007\/978-3-319-18038-0_49"},{"key":"10.1016\/j.neucom.2021.03.051_b0070","doi-asserted-by":"crossref","unstructured":"Y. Fang, B.P. Hsu, K.C. Chang, Confidence-aware graph regularization with heterogeneous pairwise features, in: W.R. Hersh, J. Callan, Y. Maarek, M. Sanderson (Eds.), Proceedings of the 35th International Conference on Research and Development in Information Retrieval, SIGIR 2012, Portland, OR, USA, August 12\u201316, 2012, ACM, 2012, pp. 951\u2013960.","DOI":"10.1145\/2348283.2348410"},{"key":"10.1016\/j.neucom.2021.03.051_b0075","unstructured":"M. Orbach, K. Crammer, Graph-based transduction with confidence, in: P.A. Flach, T.D. Bie, N. 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Garnett (Eds.), Proceedings of the Annual Conference on Neural Information Processing Systems, NIPS 2015, Montreal, Quebec, Canada, December 7-12, 2015, pp. 2224\u20132232."},{"key":"10.1016\/j.neucom.2021.03.051_b0210","unstructured":"J. Atwood, D. Towsley, Diffusion-convolutional neural networks, in: D.D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, R. Garnett (Eds.), Proceedings of the Annual Conference on Neural Information Processing Systems, NIPS 2016, Barcelona, Spain, December 5\u201310, 2016, pp. 1993\u20132001."},{"key":"10.1016\/j.neucom.2021.03.051_b0215","unstructured":"M. Niepert, M. Ahmed, K. Kutzkov, Learning convolutional neural networks for graphs, in: M. Balcan, K.Q. 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