{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T18:41:42Z","timestamp":1786473702276,"version":"3.56.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032348951","type":"print"},{"value":"9783032348968","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-34896-8_28","type":"book-chapter","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T18:16:10Z","timestamp":1786472170000},"page":"368-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Sustainability in\u00a0Graph Intelligence"],"prefix":"10.1007","author":[{"given":"Pierre-Paul","family":"Cavallera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Landy","family":"Andriamampianina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moncef","family":"Garouani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franck","family":"Ravat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiefu","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathalie","family":"Vall\u00e8s-Parlangeau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","unstructured":"Ariyanti, S., Suryanegara, M., Arifin, A.S., Nurwidya, A.I., Hayati, N.: Trade-off between energy consumption and three configuration parameters in artificial intelligence (AI) training: lessons for environmental policy. Sustainability 17(12) (2025). https:\/\/doi.org\/10.3390\/su17125359","DOI":"10.3390\/su17125359"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Baruah, T., Shivdikar, K., Dong, S., Sun, Y., Mojumder, S.A.: GNNMark: a benchmark suite to characterize graph neural network training on GPUs. In: 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 13\u201323. IEEE, Stony Brook (2021). https:\/\/doi.org\/10.1109\/ISPASS51385.2021.00013","DOI":"10.1109\/ISPASS51385.2021.00013"},{"issue":"1","key":"28_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Calinski","year":"1974","unstructured":"Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1\u201327 (1974). https:\/\/doi.org\/10.1080\/03610927408827101","journal-title":"Commun. Stat. Theory Methods"},{"key":"28_CR4","doi-asserted-by":"publisher","unstructured":"Canziani, A., Paszke, A., Culurciello, E.: An Analysis of Deep Neural Network Models for Practical Applications (2017). https:\/\/doi.org\/10.48550\/arXiv.1605.07678","DOI":"10.48550\/arXiv.1605.07678"},{"key":"28_CR5","doi-asserted-by":"publisher","unstructured":"Dash, M., Liu, H., Yao, J.: Dimensionality reduction of unsupervised data. In: Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence, pp. 532\u2013539. IEEE Comput. Soc, Newport Beach (1997). https:\/\/doi.org\/10.1109\/TAI.1997.632300","DOI":"10.1109\/TAI.1997.632300"},{"key":"28_CR6","doi-asserted-by":"publisher","unstructured":"Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224\u2013227 (1979). https:\/\/doi.org\/10.1109\/TPAMI.1979.4766909","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"28_CR7","doi-asserted-by":"publisher","unstructured":"Fischer, R., et\u00a0al.: Ground-truthing ai energy consumption: validating codecarbon against external measurements. arXiv preprint (2025). https:\/\/doi.org\/10.48550\/arXiv.2509.22092","DOI":"10.48550\/arXiv.2509.22092"},{"key":"28_CR8","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jpdc.2019.07.007","volume":"134","author":"E Garc\u00eda-Mart\u00edn","year":"2019","unstructured":"Garc\u00eda-Mart\u00edn, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75\u201388 (2019). https:\/\/doi.org\/10.1016\/j.jpdc.2019.07.007","journal-title":"J. Parallel Distrib. Comput."},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Gastinger, J., Huang, S., Galkin, M., Loghmani, E., Parviz, A.: TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs (2024)","DOI":"10.52202\/079017-4450"},{"issue":"1","key":"28_CR10","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s00332-022-09863-0","volume":"33","author":"S Klus","year":"2023","unstructured":"Klus, S., Djurdjevac Conrad, N.: Koopman-based spectral clustering of directed and time-evolving graphs. J. Nonlinear Sci. 33(1), 8 (2023). https:\/\/doi.org\/10.1007\/s00332-022-09863-0","journal-title":"J. Nonlinear Sci."},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (2018). https:\/\/doi.org\/10.48550\/arXiv.1707.01926","DOI":"10.48550\/arXiv.1707.01926"},{"key":"28_CR12","unstructured":"Luccioni, A.S., et al.: Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model (2023)"},{"key":"28_CR13","doi-asserted-by":"publisher","unstructured":"Rozemberczki, B., et al.: PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (2021). https:\/\/doi.org\/10.48550\/arXiv.2104.07788","DOI":"10.48550\/arXiv.2104.07788"},{"key":"28_CR14","doi-asserted-by":"publisher","unstructured":"Schwartz, R., Dodge, J., Smith, N.A., Etzioni, O.: Green AI. Commun. ACM 63(12), 54\u201363 (2020). https:\/\/doi.org\/10.1145\/3381831","DOI":"10.1145\/3381831"},{"key":"28_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1007\/978-3-030-04167-0_33","volume-title":"Neural Information Processing","author":"Y Seo","year":"2018","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362\u2013373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04167-0_33"},{"key":"28_CR16","doi-asserted-by":"publisher","unstructured":"Taheri, A., Berger-Wolf, T.: Predictive temporal embedding of dynamic graphs. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 57\u201364. ACM, Vancouver (2019). https:\/\/doi.org\/10.1145\/3341161.3342872","DOI":"10.1145\/3341161.3342872"},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Tschand, A., Rajan, A.T.R., Idgunji, S.: MLPerf power: benchmarking the energy efficiency of machine learning systems from microwatts to megawatts for sustainable AI (2025). https:\/\/doi.org\/10.48550\/arXiv.2410.12032","DOI":"10.48550\/arXiv.2410.12032"},{"key":"28_CR18","doi-asserted-by":"publisher","unstructured":"Zhang, C., Lei, M.: A survey on spatio-temporal graph neural networks for traffic forecasting. In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1417\u20131423. IEEE, Shanghai (2023). https:\/\/doi.org\/10.1109\/ICDMW60847.2023.00180","DOI":"10.1109\/ICDMW60847.2023.00180"}],"container-title":["Lecture Notes in Computer Science","Big Data Analytics and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-34896-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T18:16:12Z","timestamp":1786472172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-34896-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"ISBN":["9783032348951","9783032348968"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-34896-8_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]},"assertion":[{"value":"12 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DaWaK","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics and Knowledge Discovery","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Graz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dawak2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/2026\/dawak2026.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}