{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:30:17Z","timestamp":1771237817664,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032164681","type":"print"},{"value":"9783032164698","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-16469-8_12","type":"book-chapter","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:57:51Z","timestamp":1771235871000},"page":"177-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparsified Neural Network Architectures Inspired by\u00a0Optimal Strongly Attack Tolerant Network Configurations"],"prefix":"10.1007","author":[{"given":"Alexander","family":"Semenov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Veremyev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donald","family":"McMann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo L.","family":"Pasiliao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir","family":"Boginski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"12_CR1","unstructured":"Ameisen, E., et al.: Circuit tracing: revealing computational graphs in language models (2025). https:\/\/transformer-circuits.pub\/2025\/attribution-graphs\/methods.html"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Atashgahi, Z., et al.: A brain-inspired algorithm for training highly sparse neural networks. Mach. Learn. 111(12), 4411\u20134452 (2022)","DOI":"10.1007\/s10994-022-06266-w"},{"issue":"8","key":"12_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011343","volume":"19","author":"S Baek","year":"2023","unstructured":"Baek, S., Park, Y., Paik, S.-B.: Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex. PLoS Comput. Biol. 19(8), e1011343 (2023)","journal-title":"PLoS Comput. Biol."},{"issue":"23","key":"12_CR4","doi-asserted-by":"publisher","first-page":"17787","DOI":"10.1007\/s00500-020-05302-y","volume":"24","author":"L Cavallaro","year":"2020","unstructured":"Cavallaro, L., Bagdasar, O., De Meo, P., Fiumara, G., Liotta, A.: Artificial neural networks training acceleration through network science strategies. Soft. Comput. 24(23), 17787\u201317795 (2020)","journal-title":"Soft. Comput."},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, H., Zhang, M., Shi, J.Q.: A survey on deep neural network pruning: taxonomy, comparison, analysis, and recommendations. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3447085"},{"issue":"2","key":"12_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.3390\/math11020321","volume":"11","author":"D Chung","year":"2023","unstructured":"Chung, D., Sohn, I.: Neural network optimization based on complex network theory: a survey. Mathematics 11(2), 321 (2023)","journal-title":"Mathematics"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Dunefsky, J., Chlenski, P., Nanda, N.: Transcoders find interpretable LLM feature circuits. In: Advances in Neural Information Processing Systems, vol. 37, pp. 24375\u201324410 (2024)","DOI":"10.52202\/079017-0768"},{"issue":"4","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1162\/netn_a_00165","volume":"4","author":"Y Hao","year":"2020","unstructured":"Hao, Y., Graham, D.: Creative destruction: sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy. Netw. Neurosci. 4(4), 1055\u20131071 (2020)","journal-title":"Netw. Neurosci."},{"key":"12_CR9","unstructured":"Lindsey, J., et al.: On the biology of a large language model (2025). https:\/\/transformer-circuits.pub\/2025\/attribution-graphs\/biology.html"},{"key":"12_CR10","unstructured":"Liu, S., et al.: More convnets in the 2020s: scaling up kernels beyond 51x51 using sparsity. arXiv preprint. arXiv:2207.03620 (2022)"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Liu, S., Mocanu, D.C., Matavalam, A.R.R., Pei, Y., Pechenizkiy, M.: Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Comput. Appl. 33, 2589\u20132604 (2021)","DOI":"10.1007\/s00521-020-05136-7"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Topological insights into sparse neural networks. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, 14\u201318 September 14\u201318 2020, Proceedings, Part III, pp. 279\u2013294. Springer (2021)","DOI":"10.1007\/978-3-030-67664-3_17"},{"key":"12_CR13","unstructured":"Liu, S., Yin, L., Mocanu, D.C., Pechenizkiy, M.: Do we actually need dense over-parameterization? In-time over-parameterization in sparse training. In: International Conference on Machine Learning, pp. 6989\u20137000. PMLR (2021)"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Duncan Luce, R.: Connectivity and generalized cliques in sociometric group structure. Psychometrika 15(2), 169\u2013190 (1950)","DOI":"10.1007\/BF02289199"},{"key":"12_CR15","doi-asserted-by":"publisher","first-page":"215705","DOI":"10.1109\/ACCESS.2020.3040943","volume":"8","author":"W Luo","year":"2020","unstructured":"Luo, W.: Improving neural network with uniform sparse connectivity. IEEE Access 8, 215705\u2013215715 (2020)","journal-title":"IEEE Access"},{"key":"12_CR16","unstructured":"Marks, S., Rager, C., Michaud, E.J., Belinkov, Y., Bau, D., Mueller, A.: Sparse feature circuits: discovering and editing interpretable causal graphs in language models. arXiv preprint arXiv:2403.19647 (2024)"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Mocanu, D.C., Mocanu, E., Stone, P., Nguyen, P.H., Gibescu, M., Liotta, A.: Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nat. Commun. 9(1), 2383 (2018)","DOI":"10.1038\/s41467-018-04316-3"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Mokken, R.J., et\u00a0al.: Cliques, clubs and clans. Qual. Quant. 13(2), 161\u2013173 (1979)","DOI":"10.1007\/BF00139635"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Shen, J., Xu, Q., Liu, J.K., Wang, Y., Pan, G., Tang, H.: ESL-SNNs: an evolutionary structure learning strategy for spiking neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 86\u201393 (2023)","DOI":"10.1609\/aaai.v37i1.25079"},{"key":"12_CR20","unstructured":"Sokar, G., Atashgahi, Z., Pechenizkiy, M., Mocanu, D.C.: Where to pay attention in sparse training for feature selection? In: Advances in Neural Information Processing Systems, vol. 35, pp. 1627\u20131642 (2022)"},{"issue":"2","key":"12_CR21","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.ejor.2011.10.027","volume":"218","author":"A Veremyev","year":"2012","unstructured":"Veremyev, A., Boginski, V.: Identifying large robust network clusters via new compact formulations of maximum K-club problems. Eur. J. Oper. Res. 218(2), 316\u2013326 (2012)","journal-title":"Eur. J. Oper. Res."},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Veremyev, A., Boginski, V.: Robustness and strong attack tolerance of low-diameter networks. In: Dynamics of Information Systems: Mathematical Foundations, pp. 137\u2013156. Springer (2012)","DOI":"10.1007\/978-1-4614-3906-6_7"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Veremyev, A., Boginski, V., Pasiliao, E.L.: Analytical characterizations of some classes of optimal strongly attack-tolerant networks and their Laplacian spectra. J. Glob. Optim. 61(1), 109\u2013138 (2015)","DOI":"10.1007\/s10898-014-0141-y"},{"issue":"99","key":"12_CR24","first-page":"1","volume":"24","author":"A Vysogorets","year":"2023","unstructured":"Vysogorets, A., Kempe, J.: Connectivity matters: neural network pruning through the lens of effective sparsity. J. Mach. Learn. Res. 24(99), 1\u201323 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"12_CR25","unstructured":"Yin, L., et\u00a0al.: Outlier weighed layerwise sparsity (OWL): a missing secret sauce for pruning LLMs to high sparsity. arXiv preprint arXiv:2310.05175 (2023)"},{"issue":"2","key":"12_CR26","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.neunet.2009.11.005","volume":"23","author":"P Zheng","year":"2010","unstructured":"Zheng, P., Tang, W., Zhang, J.: A simple method for designing efficient small-world neural networks. Neural Netw. 23(2), 155\u2013159 (2010)","journal-title":"Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Dynamics of Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16469-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:57:57Z","timestamp":1771235877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16469-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032164681","9783032164698"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16469-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"17 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Dynamics of Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"1 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 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":"dis22025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dis2025.ujep.cz\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}