{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:51:31Z","timestamp":1742914291038,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031785405"},{"type":"electronic","value":"9783031785412"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78541-2_29","type":"book-chapter","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T20:58:25Z","timestamp":1737665905000},"page":"472-488","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AmGNN: A Framework for\u00a0Adaptive Processing of\u00a0Inter-layer Information in\u00a0Multi-layer Graph"],"prefix":"10.1007","author":[{"given":"Huaisheng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxiang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suhang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., Tang, J.: Representation learning for attributed multiplex heterogeneous network. In: Proceedings of SIGKDD (2019)","DOI":"10.1145\/3292500.3330964"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: Proceedings of WWW, pp. 417\u2013426 (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Fu, D., Xu, Z., Li, B., Tong, H., He, J.: A view-adversarial framework for multi-view network embedding. In: Proceedings of CIKM (2020)","DOI":"10.1145\/3340531.3412127"},{"key":"29_CR4","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of ICML, pp. 1050\u20131059 (2016)"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Gao, H., Wang, Z., Ji, S.: Large-scale learnable graph convolutional networks. In: Proceedings of SIGKDD, pp. 1416\u20131424 (2018)","DOI":"10.1145\/3219819.3219947"},{"key":"29_CR6","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of NeurIPS (2017)"},{"key":"29_CR7","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584 (2017)"},{"key":"29_CR8","unstructured":"He, M., Wei, Z., Wen, J.R.: Convolutional neural networks on graphs with chebyshev approximation, revisited. arXiv preprint arXiv:2202.03580 (2022)"},{"key":"29_CR9","unstructured":"He, M., Wei, Z., Xu, H., et\u00a0al.: Bernnet: learning arbitrary graph spectral filters via bernstein approximation. In: Proceedings of NeurIPS (2021)"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of WWW (2016)","DOI":"10.1145\/2872427.2883037"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Jing, B., Feng, S., Xiang, Y., Chen, X., Chen, Y., Tong, H.: X-goal: multiplex heterogeneous graph prototypical contrastive learning. In: CIKM, pp. 894\u2013904 (2022)","DOI":"10.1145\/3511808.3557490"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Jing, B., Park, C., Tong, H.: HDMI: high-order deep multiplex infomax. In: Proceedings of WWW, pp. 2414\u20132424 (2021)","DOI":"10.1145\/3442381.3449971"},{"key":"29_CR13","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Proceedings of NeurIPS (2017)"},{"key":"29_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"29_CR15","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Lee, N., Lee, J., Park, C.: Augmentation-free self-supervised learning on graphs. In: Proceedings of AAAI, pp. 7372\u20137380 (2022)","DOI":"10.1609\/aaai.v36i7.20700"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, C., Tong, H., Liu, H.: Multi-layered network embedding. In: Proceedings of SDM, pp. 684\u2013692 (2018)","DOI":"10.1137\/1.9781611975321.77"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wang, S., Aggarwal, C.C., Yin, D., Tang, J.: Multi-dimensional graph convolutional networks. In: Proceedings of SDM, pp. 657\u2013665 (2019)","DOI":"10.1137\/1.9781611975673.74"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Mitra, A., Vijayan, P., Sanasam, R., Goswami, D., Parthasarathy, S., Ravindran, B.: Semi-supervised deep learning for multiplex networks. In: Proceedings of SIGKDD (2021)","DOI":"10.1145\/3447548.3467443"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Mo, Y., Peng, L., Xu, J., Shi, X., Zhu, X.: Simple unsupervised graph representation learning. In: Proceedings of AAAI (2022)","DOI":"10.1145\/3503161.3547949"},{"issue":"1","key":"29_CR21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","volume":"104","author":"M Nickel","year":"2015","unstructured":"Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11\u201333 (2015)","journal-title":"Proc. IEEE"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Park, C., Kim, D., Han, J., Yu, H.: Unsupervised attributed multiplex network embedding. In: Proceedings of AAAI, pp. 5371\u20135378 (2020)","DOI":"10.1609\/aaai.v34i04.5985"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Peng, Z., et al.: Graph representation learning via graphical mutual information maximization. In: Proceedings of WWW, pp. 259\u2013270 (2020)","DOI":"10.1145\/3366423.3380112"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Qu, L., Zhu, H., Zheng, R., Shi, Y., Yin, H.: Imgagn: imbalanced network embedding via generative adversarial graph networks. In: Proceedings of SIGKDD (2021)","DOI":"10.1145\/3447548.3467334"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Qu, M., Tang, J., Shang, J., Ren, X., Zhang, M., Han, J.: An attention-based collaboration framework for multi-view network representation learning. In: Proceedings of CIKM, pp. 1767\u20131776 (2017)","DOI":"10.1145\/3132847.3133021"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Hsieh, T.Y., Tang, X., Honavar, V.: Megan: a generative adversarial network for multi-view network embedding. In: Proceedings of IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/489"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of SIGKDD, pp. 990\u2013998 (2008)","DOI":"10.1145\/1401890.1402008"},{"key":"29_CR28","unstructured":"Tang, S., Li, B., Yu, H.: Chebnet: efficient and stable constructions of deep neural networks with rectified power units using chebyshev approximations. arXiv preprint arXiv:1911.05467 (2019)"},{"key":"29_CR29","unstructured":"Tsitsulin, A., Palowitch, J., Perozzi, B., M\u00fcller, E.: Graph clustering with graph neural networks. arXiv preprint arXiv:2006.16904 (2020)"},{"key":"29_CR30","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":"29_CR31","unstructured":"Wang, H., Zhang, J., Zhu, Q., Huang, W.: Augmentation-free graph contrastive learning with performance guarantee. arXiv preprint arXiv:2204.04874 (2022)"},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., Yu, P.S.: Heterogeneous graph attention network. In: Proceedings of WWW (2019)","DOI":"10.1145\/3308558.3313562"},{"key":"29_CR33","unstructured":"Wang, X., Zhang, M.: How powerful are spectral graph neural networks. In: Proceedings of ICML, pp. 23341\u201323362 (2022)"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Xiao, T., Chen, Z., Wang, D., Wang, S.: Learning how to propagate messages in graph neural networks. In: Proceedings of SIGKDD, pp. 1894\u20131903 (2021)","DOI":"10.1145\/3447548.3467451"},{"key":"29_CR35","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: Proceedings of ICLR (2018)"},{"key":"29_CR36","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Proceedings of NeurIPS (2018)"},{"key":"29_CR37","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: Proceedings of NeurIPS (2020)"}],"container-title":["Lecture Notes in Computer Science","Social Networks Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78541-2_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T20:58:56Z","timestamp":1737665936000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78541-2_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031785405","9783031785412"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78541-2_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ASONAM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Social Networks Analysis and Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rende","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asonam-12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/asonam.cpsc.ucalgary.ca\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}