{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:51:52Z","timestamp":1783702312662,"version":"3.55.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434174","type":"print"},{"value":"9783031434181","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43418-1_6","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T09:02:26Z","timestamp":1694854946000},"page":"87-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Boosting Adaptive Graph Augmented MLPs via\u00a0Customized Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Shaowei","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengwei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Bo, D., Wang, X., Shi, C., Shen, H.: Beyond low-frequency information in graph convolutional networks. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"6_CR2","unstructured":"Chen, L., Chen, Z., Bruna, J.: On graph neural networks versus graph-augmented MLPs. In: ICLR (2021)"},{"key":"6_CR3","unstructured":"Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized pagerank graph neural network. In: ICLR (2021)"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Geng, T., et al.: AWB-GCN: a graph convolutional network accelerator with runtime workload rebalancing. In: MICRO (2020)","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"6_CR5","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS (2017)"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"He, H., Wang, J., Zhang, Z., Wu, F.: Compressing deep graph neural networks via adversarial knowledge distillation. arXiv preprint: arXiv:2205.11678 (2022)","DOI":"10.1145\/3534678.3539315"},{"key":"6_CR7","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint: arXiv:1503.02531 (2015)"},{"key":"6_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint: arXiv:1412.6980 (2014)"},{"key":"6_CR9","unstructured":"Li, P., Chien, I., Milenkovic, O.: Optimizing generalized pagerank methods for seed-expansion community detection. In: NeurIPS (2019)"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TC.2020.3014632","volume":"70","author":"S Liang","year":"2021","unstructured":"Liang, S., et al.: EnGN: a high-throughput and energy-efficient accelerator for large graph neural networks. IEEE Trans. Comput. 70, 1511\u20131525 (2021)","journal-title":"IEEE Trans. Comput."},{"key":"6_CR11","unstructured":"Lim, D., Li, X., Hohne, F., Lim, S.: New benchmarks for learning on non-homophilous graphs (2021). https:\/\/arxiv.org\/abs\/2104.01404"},{"key":"6_CR12","unstructured":"Liu, H., Dai, Z., So, D.R., Le, Q.V.: Pay attention to MLPs. In: NeurIPS (2021)"},{"key":"6_CR13","unstructured":"Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.W., Precup, D.: Is heterophily a real nightmare for graph neural networks to do node classification? arXiv preprint: arXiv:2109.05641 (2021)"},{"issue":"1","key":"6_CR14","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","volume":"27","author":"M McPherson","year":"2001","unstructured":"McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415\u2013444 (2001)","journal-title":"Ann. Rev. Sociol."},{"key":"6_CR15","unstructured":"Pei, H., Wei, B., Chang, K.C.C., Lei, Y., Yang, B.: Geom-GCN: geometric graph convolutional networks. In: ICLR (2019)"},{"key":"6_CR16","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. In: ICLR (2015)"},{"key":"6_CR17","unstructured":"Rossi, E., Frasca, F., Chamberlain, B., Eynard, D., Bronstein, M.M., Monti, F.: SIGN: scalable inception graph neural networks (2020). https:\/\/arxiv.org\/abs\/2004.11198"},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","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. IEEE Sig. Process, Mag. 30, 83\u201398 (2013)","journal-title":"IEEE Sig. Process, Mag."},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"6_CR20","unstructured":"Tian, Y., Zhang, C., Guo, Z., Zhang, X., Chawla, N.: Learning MLPs on graphs: a unified view of effectiveness, robustness, and efficiency. In: ICLR (2023)"},{"key":"6_CR21","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint: arXiv:1710.10903 (2017)"},{"key":"6_CR22","unstructured":"Welling, M., Kipf, T.N.: Semi-supervised classification with graph convolutional networks. In: ICLR (2016)"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Yan, B., Wang, C., Guo, G., Lou, Y.: TinyGNN: learning efficient graph neural networks. In: KDD (2020)","DOI":"10.1145\/3394486.3403236"},{"key":"6_CR24","unstructured":"Yang, C., Wu, Q., Wang, J., Yan, J.: Graph neural networks are inherently good generalizers: isights by bridging GNNs and MLPs. In: ICLR (2023)"},{"key":"6_CR25","unstructured":"Yang, C., Wu, Q., Yan, J.: Geometric knowledge distillation: topology compression for graph neural networks. In: NeurIPS (2023)"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X.: Distilling knowledge from graph convolutional networks. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"6_CR27","unstructured":"Zeng, H., Zhou, H., Srivastava, A., Kannan, R., Prasanna, V.K.: Graphsaint: Graph sampling based inductive learning method. In: ICLR (2020)"},{"key":"6_CR28","unstructured":"Zhang, S., Liu, Y., Sun, Y., Shah, N.: Graph-less neural networks: teaching old MLPs new tricks via distillation. In: ICLR (2022)"},{"key":"6_CR29","unstructured":"Zhao, Y., Wang, D., Bates, D., Mullins, R.D., Jamnik, M., Li\u00f2, P.: Learned low precision graph neural networks (2020). https:\/\/arxiv.org\/abs\/2009.09232"},{"key":"6_CR30","unstructured":"Zheng, W., Huang, E.W., Rao, N., Katariya, S., Wang, Z., Subbian, K.: Cold brew: distilling graph node representations with incomplete or missing neighborhoods. In: ICLR (2022)"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Zhou, H., Srivastava, A., Zeng, H., Kannan, R., Prasanna, V.: Accelerating large scale real-time GNN inference using channel pruning. VLDB (2021)","DOI":"10.14778\/3461535.3461547"},{"key":"6_CR32","unstructured":"Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., Koutra, D.: Beyond homophily in graph neural networks: current limitations and effective designs. In: NeurIPS (2020)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43418-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:04:00Z","timestamp":1719407040000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43418-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434174","9783031434181"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43418-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"As machine learning and data mining researchers, we recognize the importance of ethical considerations in our work. The ethical implications of our research can have a significant impact on individuals, communities, and society as a whole. Therefore, we believe that it is our responsibility to carefully consider and address any ethical concerns that may arise from our work. We acknowledge that the collection and processing of personal data can have significant ethical implications. As such, we have taken steps to ensure that our research adheres to ethical guidelines and regulations. We have obtained all necessary permissions and have taken encryption measures to protect the privacy and confidentiality of any personal data used in our research. Additionally, we have implemented measures to ensure that any inferences made from data are transparent and are not used to perpetuate any forms of bias or discrimination. Our research aims to provide insights that are beneficial to society, while avoiding any potential negative impacts on individuals or communities. Our research does not relate to, nor collaborate with, the police or military. We believe that by addressing ethical concerns in our work, we can promote the responsible and beneficial use of machine learning and data mining technologies.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"3.63","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":"4.5","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":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","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)"}}]}}