{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:29:04Z","timestamp":1742945344503,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031534676"},{"type":"electronic","value":"9783031534683"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53468-3_1","type":"book-chapter","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T13:03:57Z","timestamp":1708347837000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Network Design Through Graph Neural Networks: Identifying Challenges and\u00a0Improving Performance"],"prefix":"10.1007","author":[{"given":"Donald","family":"Loveland","sequence":"first","affiliation":[]},{"given":"Rajmonda","family":"Caceres","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Soc. 25, 163\u2013177 (2001)","DOI":"10.1080\/0022250X.2001.9990249"},{"key":"1_CR2","unstructured":"Chami, I., Ying, Z., R\u00e9, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. In: NeurIPS, vol. 32 (2019)"},{"issue":"5","key":"1_CR3","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1007\/s10618-015-0447-5","volume":"30","author":"H Chan","year":"2016","unstructured":"Chan, H., Akoglu, L.: Optimizing network robustness by edge rewiring: a general framework. Data Min. Knowl. Discov. 30(5), 1395\u20131425 (2016)","journal-title":"Data Min. Knowl. Discov."},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1080\/10095020.2019.1568736","volume":"22","author":"M Domingo","year":"2019","unstructured":"Domingo, M., Thibaud, R., Claramunt, C.: A graph-based approach for the structural analysis of road and building layouts. Geo-spatial Inf. Sci. 22(1), 59\u201372 (2019)","journal-title":"Geo-spatial Inf. Sci."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Enoch, S., Mendon\u00e7a, J., Hong, J., Ge, M., Kim, D.S.: An integrated security hardening optimization for dynamic networks using security and availability modeling with multi-objective algorithm. Comp. Netw. 208, 108864 (2022)","DOI":"10.1016\/j.comnet.2022.108864"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Erd, F., Vignatti, A., da Silva, M.V.G.: The generalized influence blocking maximization problem. Soc. Netw. Anal. Mining (2021)","DOI":"10.1007\/s13278-021-00765-9"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Fan, W., et al.: Graph neural networks for social recommendation. In: WWW, pp. 417\u2013426 (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"1_CR8","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. CoRR (2017)"},{"key":"1_CR9","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. CoRR (2017)"},{"key":"1_CR10","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax (2017)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Jin, W., Ma, Y., Liu, X., Tang, X., Wang, S., Tang, J.: Graph structure learning for robust graph neural networks. In: SIGKDD (2020)","DOI":"10.1145\/3394486.3403049"},{"key":"1_CR12","unstructured":"Jin, W., Barzilay, R., Jaakkola, T.: Junction tree variational autoencoder for molecular graph generation. In: ICML, PMLR (2018)"},{"key":"1_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"1_CR14","unstructured":"Kun, J., Caceres, R.S., Carter, K.M.: Locally boosted graph aggregation for community detection. arXiv preprint arXiv:1405.3210 (2014)"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Laishram, R., Sariy\u00fcce, A., Eliassi-Rad, T., Pinar, A., Soundarajan, S.: Measuring and improving the core resilience of networks (2018)","DOI":"10.1145\/3178876.3186127"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Li, D., Eliassi-Rad, T., Zhang, H.R.: Optimal intervention on weighted networks via edge centrality. In: 5th International Workshop on Epidemiology Meets Data Mining and Knowledge Discovery at KDD (2022)","DOI":"10.1137\/1.9781611977653.ch48"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Li, G., Duda, M., Zhang, X., Koutra, D., Yan, Y.: Interpretable sparsification of brain graphs: better practices and effective designs for graph neural networks. arXiv preprint arXiv:2306.14375 (2023)","DOI":"10.1145\/3580305.3599394"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Safavi, T., Dighe, A., Koutra, D.: Graph summarization methods and applications: a survey. ACM Comput. Surv. 51(3), 1\u201334 (2018)","DOI":"10.1145\/3186727"},{"key":"1_CR19","unstructured":"Lucic, A., ter Hoeve, M., Tolomei, G., de Rijke, M., Silvestri, F.: Counterfactual explanations for graph neural networks. CoRR, Cfgnnexplainer (2021)"},{"key":"1_CR20","unstructured":"Luo, D., et al.: Parameterized explainer for graph neural network. In: NeurIPS (2020)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Miller, B.A., Shafi, Z., Ruml, W., Vorobeychik, Y., Eliassi-Rad, T., Alfeld, S.: Pathattack: attacking shortest paths in complex networks. In: ECML-PKDD, pp. 532\u2013547 (2021)","DOI":"10.1007\/978-3-030-86520-7_33"},{"key":"1_CR22","unstructured":"Wei, Z., Chen, M., Ding, B., Huang, Z., Li, Y.: Simple and deep graph convolutional networks. In: ICML (2020)"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Morales, P., Caceres, R., Eliassi-Rad, T.: Selective network discovery via deep reinforcement learning on embedded spaces. Appl. Netw. Sci. (2021)","DOI":"10.1007\/s41109-021-00365-8"},{"key":"1_CR24","unstructured":"Schlichtkrull, M.S., Cao, N.D., Titov, I.: Interpreting graph neural networks for NLP with differentiable edge masking. In: ICLR (2021)"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Sharma, S., Sharma, R.: Forecasting transactional amount in bitcoin network using temporal GNN approach. In: ASONAM (2020)","DOI":"10.1109\/ASONAM49781.2020.9381363"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"\u015eim\u015fek, \u00d6., Jensen, D.: Navigating networks by using homophily and degree. Proc. Natl. Acad. Sci. 105(35), 12758\u201312762 (2008)","DOI":"10.1073\/pnas.0800497105"},{"key":"1_CR27","doi-asserted-by":"publisher","unstructured":"Smith, J.C., Prince, M., Geunes, J.: Modern network interdiction problems and algorithms. In: Pardalos, P.M., Du, D.-Z., Graham, R.L. (eds.) Handbook of Combinatorial Optimization, pp. 1949\u20131987. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4419-7997-1_61","DOI":"10.1007\/978-1-4419-7997-1_61"},{"key":"1_CR28","doi-asserted-by":"publisher","unstructured":"V\u00e4is\u00e4l\u00e4, J.: Gromov hyperbolic spaces. Exposition. Math. 23(3), 187\u2013231 (2005). https:\/\/doi.org\/10.1016\/j.exmath.2005.01.010","DOI":"10.1016\/j.exmath.2005.01.010"},{"key":"1_CR29","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Generating explanations for graph neural networks. In: NeurIPS, Gnnexplainer (2019)"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Kearnes, S., Li, L., Zare, R.N., Riley, P.: Optimization of molecules via deep reinforcement learning. Sci. Rep. (2019)","DOI":"10.1038\/s41598-019-47148-x"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Zhu, H., Gupta, V., Ahuja, S.S., Tian, Y., Zhang, Y., Jin, X.: Network planning with deep reinforcement learning. (2021)","DOI":"10.1145\/3452296.3472902"}],"container-title":["Studies in Computational Intelligence","Complex Networks & Their Applications XII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53468-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T13:07:10Z","timestamp":1718629630000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53468-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031534676","9783031534683"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53468-3_1","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COMPLEX NETWORKS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Complex Networks and Their Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Menton","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwcna2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.complexnetworks.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}