{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:50:08Z","timestamp":1763013008335,"version":"3.45.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032079039","type":"print"},{"value":"9783032079046","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-07904-6_10","type":"book-chapter","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:48:17Z","timestamp":1763012897000},"page":"104-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GraphTreeGen: Subtree-Centric Approach to\u00a0Efficient and\u00a0Supervised Graph Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6654-6124","authenticated-orcid":false,"given":"Yitong","family":"Luo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5595-6673","authenticated-orcid":false,"given":"Islem","family":"Rekik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"102090","DOI":"10.1016\/j.media.2021.102090","volume":"72","author":"A Bessadok","year":"2021","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Brain multigraph prediction using topology-aware adversarial graph neural network. Med. Image Anal. 72, 102090 (2021)","journal-title":"Med. Image Anal."},{"issue":"5","key":"10_CR2","doi-asserted-by":"publisher","first-page":"5833","DOI":"10.1109\/TPAMI.2022.3209686","volume":"45","author":"A Bessadok","year":"2022","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5833\u20135848 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10_CR3","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968\u2013980 (2006)","journal-title":"Neuroimage"},{"issue":"6","key":"10_CR4","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: Freesurfer. Neuroimage 62(2), 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"10_CR6","unstructured":"Gupta, M., Jain, S., Ramani, V., Kodamana, H., Ranu, S.: Bonsai: gradient-free graph distillation for node classification. arXiv preprint arXiv:2410.17579 (2024)"},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"Isallari, M., Rekik, I.: GSR-Net: graph super-resolution network for predicting high-resolution from low-resolution functional brain connectomes. In: International Workshop on Machine Learning in Medical Imaging, pp. 139\u2013149. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-59861-7_15","DOI":"10.1007\/978-3-030-59861-7_15"},{"key":"10_CR8","unstructured":"Jin, W., Barzilay, R., Jaakkola, T.: Junction tree variational autoencoder for molecular graph generation. In: International Conference on Machine Learning, pp. 2323\u20132332. PMLR (2018)"},{"key":"10_CR9","unstructured":"Kim, B.H., Ye, J.C., Kim, J.J.: Learning dynamic graph representation of brain connectome with spatio-temporal attention. In: Advances in Neural Information Processing Systems, vol. 34, pp. 4314\u20134327 (2021)"},{"key":"10_CR10","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"issue":"11","key":"10_CR11","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1038\/nn.3837","volume":"17","author":"JW Lichtman","year":"2014","unstructured":"Lichtman, J.W., Pfister, H., Shavit, N.: The big data challenges of connectomics. Nat. Neurosci. 17(11), 1448\u20131454 (2014)","journal-title":"Nat. Neurosci."},{"issue":"4","key":"10_CR12","doi-asserted-by":"publisher","first-page":"567","DOI":"10.3390\/e25040567","volume":"25","author":"M Lin","year":"2023","unstructured":"Lin, M., Wen, K., Zhu, X., Zhao, H., Sun, X.: Graph autoencoder with preserving node attribute similarity. Entropy 25(4), 567 (2023)","journal-title":"Entropy"},{"key":"10_CR13","unstructured":"Liu, C., et al.: Generative diffusion models on graphs: methods and applications. arXiv preprint arXiv:2302.02591 (2023)"},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"Mhiri, I., Nebli, A., Mahjoub, M.A., Rekik, I.: Non-isomorphic inter-modality graph alignment and synthesis for holistic brain mapping. In: International Conference on Information Processing in Medical Imaging, pp. 203\u2013215. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_16","DOI":"10.1007\/978-3-030-78191-0_16"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer\u2019s disease. Med. Image Anal. 48, 117\u2013130 (2018)","journal-title":"Med. Image Anal."},{"issue":"8","key":"10_CR16","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1002\/hbm.22822","volume":"36","author":"JB Pereira","year":"2015","unstructured":"Pereira, J.B., et al.: Aberrant cerebral network topology and mild cognitive impairment in early parkinson\u2019s disease. Hum. Brain Mapp. 36(8), 2980\u20132995 (2015)","journal-title":"Hum. Brain Mapp."},{"issue":"8","key":"10_CR17","doi-asserted-by":"publisher","first-page":"3476","DOI":"10.1093\/cercor\/bhw128","volume":"26","author":"JB Pereira","year":"2016","unstructured":"Pereira, J.B., et al.: Disrupted network topology in patients with stable and progressive mild cognitive impairment and alzheimer\u2019s disease. Cereb. Cortex 26(8), 3476\u20133493 (2016)","journal-title":"Cereb. Cortex"},{"key":"10_CR18","doi-asserted-by":"publisher","unstructured":"Rajadhyaksha, N., Rekik, I.: Diffusion-based graph super-resolution with application to connectomics. In: International Workshop on PRedictive Intelligence In MEdicine, pp. 96\u2013107. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-46005-0_9","DOI":"10.1007\/978-3-031-46005-0_9"},{"key":"10_CR19","unstructured":"Said, A., et al.: Neurograph: benchmarks for graph machine learning in brain connectomics. In: Advances in Neural Information Processing Systems, vol. 36, pp. 6509\u20136531 (2023)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3693\u20133702 (2017)","DOI":"10.1109\/CVPR.2017.11"},{"key":"10_CR21","doi-asserted-by":"publisher","unstructured":"Singh, P., Rekik, I.: Strongly topology-preserving GNNs for brain graph super-resolution. In: International Workshop on PRedictive Intelligence In MEdicine, pp. 124\u2013136. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-74561-4_11","DOI":"10.1007\/978-3-031-74561-4_11"},{"issue":"1","key":"10_CR22","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1111\/j.1749-6632.2010.05888.x","volume":"1224","author":"O Sporns","year":"2011","unstructured":"Sporns, O.: The human connectome: a complex network. Ann. N. Y. Acad. Sci. 1224(1), 109\u2013125 (2011)","journal-title":"Ann. N. Y. Acad. Sci."},{"issue":"2","key":"10_CR23","doi-asserted-by":"publisher","first-page":"111","DOI":"10.31887\/DCNS.2018.20.2\/osporns","volume":"20","author":"O Sporns","year":"2018","unstructured":"Sporns, O.: Graph theory methods: applications in brain networks. Dialogues Clin. Neurosci. 20(2), 111\u2013121 (2018)","journal-title":"Dialogues Clin. Neurosci."},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: GraphGAN: graph representation learning with generative adversarial nets. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Xiao, S., Rekik, I.: DynGNN: dynamic memory-enhanced generative GNNs for predicting temporal brain connectivity. In: International Workshop on PRedictive Intelligence In MEdicine, pp. 111\u2013123. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-74561-4_10","DOI":"10.1007\/978-3-031-74561-4_10"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Zong, Y., Wang, S.: Brainnetdiff: generative AI empowers brain network generation via multimodal diffusion model. arXiv preprint arXiv:2311.05199 (2023)","DOI":"10.1109\/ISBI56570.2024.10635395"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07904-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:48:20Z","timestamp":1763012900000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07904-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"ISBN":["9783032079039","9783032079046"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07904-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"14 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on PRedictive Intelligence In MEdicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"prime2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/basira-lab.com\/prime-miccai-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}