{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:31:16Z","timestamp":1783787476598,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NIH","award":["R01MH105561"],"award-info":[{"award-number":["R01MH105561"]}]},{"name":"ONR","award":["N00014-18-1-2009"],"award-info":[{"award-number":["N00014-18-1-2009"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3542680","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"4743-4751","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning"],"prefix":"10.1145","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hejie","family":"Cui","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Kan","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"He","sequence":"additional","affiliation":[{"name":"Lehigh University, Bethlehem, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Guo","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl","family":"Yang","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Principal component analysis","author":"Abdi Herv\u00e9","year":"2010","unstructured":"Herv\u00e9 Abdi and Lynne J Williams. 2010. Principal component analysis. Wiley interdisciplinary reviews: computational statistics, Vol. 2, 4 (2010), 433--459."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Alessandro Achille Michael Lam Rahul Tewari Avinash Ravichandran Subhransu Maji Charless C Fowlkes Stefano Soatto and Pietro Perona. 2019. Task2vec: Task embedding for meta-learning. In ICCV. 6430--6439.","DOI":"10.1109\/ICCV.2019.00653"},{"key":"e_1_3_2_2_3_1","unstructured":"Sungyong Baik Myungsub Choi Janghoon Choi Heewon Kim and Kyoung Mu Lee. 2020. Meta-learning with adaptive hyperparameters. In NeurIPS."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1903070116"},{"key":"e_1_3_2_2_5_1","unstructured":"Davide Buffelli and Fabio Vandin. 2020. A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings."},{"key":"e_1_3_2_2_6_1","unstructured":"Jatin Chauhan Deepak Nathani and Manohar Kaul. 2020. Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures. In ICLR."},{"key":"e_1_3_2_2_7_1","volume-title":"Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, and Carl Yang. 2022 a. BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks. arXiv preprint arXiv:2204.07054","author":"Cui Hejie","year":"2022","unstructured":"Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, and Carl Yang. 2022 a. BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks. arXiv preprint arXiv:2204.07054 (2022)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Hejie Cui Wei Dai Yanqiao Zhu Xiaoxiao Li Lifang He and Carl Yang. 2022 b. BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis. In MICCAI.","DOI":"10.1007\/978-3-031-16452-1_36"},{"key":"e_1_3_2_2_9_1","volume-title":"On positional and structural node features for graph neural networks on non-attributed graphs. arXiv preprint arXiv:2107.01495","author":"Cui Hejie","year":"2021","unstructured":"Hejie Cui, Zijie Lu, Pan Li, and Carl Yang. 2021. On positional and structural node features for graph neural networks on non-attributed graphs. arXiv preprint arXiv:2107.01495 (2021)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjid.2016.05.008"},{"key":"e_1_3_2_2_11_1","volume-title":"In\u00eas Chendo, Ana Castro Caldas, Sofia Reim ao, Ricardo M Fernandes, Jos\u00e9 Vale, Michele Tinazzi, Kailash Bhatia, and Joaquim J Ferreira.","author":"Faustino Patr\u00edcia R","year":"2020","unstructured":"Patr\u00edcia R Faustino, Goncc alo S Duarte, In\u00eas Chendo, Ana Castro Caldas, Sofia Reim ao, Ricardo M Fernandes, Jos\u00e9 Vale, Michele Tinazzi, Kailash Bhatia, and Joaquim J Ferreira. 2020. Risk of developing Parkinson disease in bipolar disorder: a systematic review and meta-analysis. JAMA neurology, Vol. 77, 2 (2020), 192--198."},{"key":"e_1_3_2_2_12_1","unstructured":"Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In ICML. 1126--1135."},{"key":"e_1_3_2_2_13_1","volume-title":"A systematic survey on deep generative models for graph generation. arXiv preprint arXiv:2007.06686","author":"Guo Xiaojie","year":"2020","unstructured":"Xiaojie Guo and Liang Zhao. 2020. A systematic survey on deep generative models for graph generation. arXiv preprint arXiv:2007.06686 (2020)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450112"},{"key":"e_1_3_2_2_15_1","unstructured":"Xuan Kan Hejie Cui Joshua Lukemire Ying Guo and Carl Yang. 2022. FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation. In MIDL."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"e_1_3_2_2_18_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_2_19_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017a. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_2_20_1","unstructured":"Qimai Li Zhichao Han and Xiao-Ming Wu. 2018. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI. 3538--3545."},{"key":"e_1_3_2_2_21_1","volume-title":"2021 a. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Med Image Anal","author":"Li Xiaoxiao","year":"2021","unstructured":"Xiaoxiao Li, Yuan Zhou, Nicha Dvornek, Muhan Zhang, Siyuan Gao, Juntang Zhuang, Dustin Scheinost, Lawrence H Staib, Pamela Ventola, and James S Duncan. 2021 a. BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis. Med Image Anal (2021)."},{"key":"e_1_3_2_2_22_1","volume-title":"SGDR: Stochastic Gradient Descent with Warm Restarts. In ICLR.","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. SGDR: Stochastic Gradient Descent with Warm Restarts. In ICLR."},{"key":"e_1_3_2_2_23_1","volume-title":"Ragin","author":"Ma Guixiang","year":"2017","unstructured":"Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, and Ann B. Ragin. 2017. Multi-view Clustering with Graph Embedding for Connectome Analysis. In CIKM. 127--136."},{"key":"e_1_3_2_2_24_1","unstructured":"Ning Ma Jiajun Bu Jieyu Yang Zhen Zhang Chengwei Yao Zhi Yu Sheng Zhou and Xifeng Yan. 2020. Adaptive-step graph meta-learner for few-shot graph classification. In CIKM. 1055--1064."},{"key":"e_1_3_2_2_25_1","volume-title":"in ICML Workshop on Deep Learning for Audio, Speech and Language Processing.","author":"Maas Andrew L.","unstructured":"Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In in ICML Workshop on Deep Learning for Audio, Speech and Language Processing."},{"key":"e_1_3_2_2_26_1","volume-title":"Bipolar disorder is associated with HIV transmission risk behavior among patients in treatment for HIV. AIDS and behavior","author":"Meade Christina S","year":"2012","unstructured":"Christina S Meade, Lisa A Bevilacqua, and Mary D Key. 2012. Bipolar disorder is associated with HIV transmission risk behavior among patients in treatment for HIV. AIDS and behavior, Vol. 16, 8 (2012), 2267--2271."},{"key":"e_1_3_2_2_27_1","volume-title":"Hinton","author":"Nair Vinod","year":"2010","unstructured":"Vinod Nair and Geoffrey E. Hinton. 2010. Rectified Linear Units Improve Restricted Boltzmann Machines. In ICML. 807--814."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1590\/s1980-5764-2016dn1004018"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Gregory M Pontone and Giacomo Koch. 2019. An association between bipolar disorder and Parkinson disease: When mood makes you move. 1125--1126 pages.","DOI":"10.1212\/WNL.0000000000007641"},{"key":"e_1_3_2_2_31_1","volume-title":"Rapid learning or feature reuse? towards understanding the effectiveness of maml. arXiv preprint arXiv:1909.09157","author":"Raghu Aniruddh","year":"2019","unstructured":"Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals. 2019. Rapid learning or feature reuse? towards understanding the effectiveness of maml. arXiv preprint arXiv:1909.09157 (2019)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/495\/1\/012003"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2001.0978"},{"key":"e_1_3_2_2_34_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_2_35_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR."},{"key":"e_1_3_2_2_36_1","volume-title":"Ken-ichi Kawarabayashi, and Stefanie Jegelka.","author":"Xu Keyulu","year":"2021","unstructured":"Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2021. How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Liang Zhan Jiayu Zhou Yalin Wang Yan Jin Neda Jahanshad Gautam Prasad Talia M Nir Cassandra D Leonardo Jieping Ye Paul M Thompson et al. 2015. Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease. Frontiers in aging neuroscience (2015).","DOI":"10.3389\/fnagi.2015.00048"},{"key":"e_1_3_2_2_38_1","volume-title":"AMIA","volume":"2018","author":"Zhang Xi","year":"2018","unstructured":"Xi Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, and Fei Wang. 2018. Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinsontextquoterights Disease. AMIA, Vol. 2018 (Dec. 2018), 1147--1156."},{"key":"e_1_3_2_2_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Zhu Qi","year":"2021","unstructured":"Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, and Jiawei Han. 2021. Transfer learning of graph neural networks with ego-graph information maximization. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_2_40_1","unstructured":"Yanqiao Zhu Hejie Cui Lifang He Lichao Sun and Carl Yang. 2022 a. Joint embedding of structural and functional brain networks with graph neural networks for mental illness diagnosis. In EMBC."},{"key":"e_1_3_2_2_41_1","volume-title":"2022 b. A Survey on Deep Graph Generation: Methods and Applications. arXiv preprint arXiv:2203.06714","author":"Zhu Yanqiao","year":"2022","unstructured":"Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, and Shu Wu. 2022 b. A Survey on Deep Graph Generation: Methods and Applications. arXiv preprint arXiv:2203.06714 (2022)."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542680","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3542680","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3542680","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:53Z","timestamp":1750186973000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":41,"alternative-id":["10.1145\/3534678.3542680","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3542680","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}