LEADER 05740nam 22006015 450 001 996418261603316 005 20200703075629.0 010 $a3-030-37157-3 024 7 $a10.1007/978-3-030-37157-9 035 $a(CKB)4100000010473962 035 $a(DE-He213)978-3-030-37157-9 035 $a(MiAaPQ)EBC6120131 035 $a(PPN)242980171 035 $a(EXLCZ)994100000010473962 100 $a20200222d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNetwork Algorithms, Data Mining, and Applications$b[electronic resource] $eNET, Moscow, Russia, May 2018 /$fedited by Ilya Bychkov, Valery A. Kalyagin, Panos M. Pardalos, Oleg Prokopyev 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 244 p. 65 illus., 43 illus. in color.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v315 311 $a3-030-37156-5 327 $a Part I: Network algorithms -- Obaid, H. B. and Trafalis, T: Fairness in Resource Allocation: Foundation and Applications -- Ignatov, D., Ivanova, P., Zamaletdinova, A. and Prokopyev, O: Searching for Maximum Quasi-Bicliques with Mixed Integer Programming -- Miasnikof, P., Pitsoulis, L., Bonner, A. J., Lawryshyn, Y. and Pardalos, P. M: Graph Clustering Via Intra-Cluster Density Maximization -- Shvydun, S.: Computational Complexity of SRIC and LRIC indices -- Sifaleras, A. and Konstantaras, I: A survey on variable neighborhood search methods for supply network inventory -- Part II: Network Data Mining -- Ananyeva, M. and Makarov, I: GSM: Inductive Learning on Dynamic Graph Embeddings -- Averchenkova, A., Akhmetzyanova, A., Sudarikov, K., Sulimov, P., Makarov I. and Zhukov, L. E: Collaborator Recommender System based on Co-authorship Network Analysis -- Demochkin, K. and Savchenko, A: User Preference Prediction in a Set of Photos based on Neural Aggregation Network -- Makrushin , S.: Network structure and scheme analysis of the Russian language segment of Wikipedia -- Meshcheryakova, N., Shvydun, S. and Aleskerov, F: Indirect Influence Assessment in the Context of Retail Food Network -- Sokolova, A. D. and Savchenko, A. V: Facial clustering in video data using deep convolutional neural networks -- Part III: Network Applications -- Egamov, A.: The existence and uniqueness theorem for initial-boundary value problem of the same class of integro-differential PDEs -- Gradoselskaya, G., Karpov, I. and Shcheglova, T: Mapping of politically active groups on social networks of Russian regions (on the example of Karachay-Cherkessia Republic) -- Mikhailova, O., Gradoselskaya, G. and Kharlamov, A: Social Mechanisms of the Subject Area Formation. The Case of ?Digital Economy -- Shcheglova, T., Gradoselskaya, G. and Karpov, I: Methodology for measuring polarization of political discourse: case of comparing oppositional and patriotic discourse in online social networks -- Zaytsev, D., Khvatsky, G., Talovsky, N. and Kuskova, V: Network Analysis Methodology of Policy Actors Identification and Power Evaluation (the case of the Unified State Exam introduction in Russia). 330 $aThis proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v315 606 $aMathematical optimization 606 $aNeural networks (Computer science)  606 $aCombinatorics 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 606 $aCombinatorics$3https://scigraph.springernature.com/ontologies/product-market-codes/M29010 615 0$aMathematical optimization. 615 0$aNeural networks (Computer science) . 615 0$aCombinatorics. 615 14$aOptimization. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aCombinatorics. 676 $a658.4032 702 $aBychkov$b Ilya$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKalyagin$b Valery A$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPardalos$b Panos M$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aProkopyev$b Oleg$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418261603316 996 $aNetwork Algorithms, Data Mining, and Applications$91921346 997 $aUNISA LEADER 03098nam 2200625 450 001 996466505103316 005 20220425091505.0 010 $a1-280-95164-8 010 $a9786610951642 010 $a3-540-73510-0 024 7 $a10.1007/978-3-540-73510-6 035 $a(CKB)1000000000437257 035 $a(EBL)3037321 035 $a(SSID)ssj0000301328 035 $a(PQKBManifestationID)11247558 035 $a(PQKBTitleCode)TC0000301328 035 $a(PQKBWorkID)10261061 035 $a(PQKB)11180946 035 $a(DE-He213)978-3-540-73510-6 035 $a(MiAaPQ)EBC3037321 035 $a(MiAaPQ)EBC6696265 035 $a(Au-PeEL)EBL6696265 035 $a(PPN)123163536 035 $a(EXLCZ)991000000000437257 100 $a20220425d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLaplacian eigenvectors of graphs $ePerron-Frobenius and Faber-Krahn type theorems /$fTu?rker B?y?kog?lu, Josef Leydold, Peter F. Stadler 205 $a1st ed. 2007. 210 1$aBerlin ;$aHeidelberg ;$aNew York :$cSpringer,$d[2007] 210 4$d©2007 215 $a1 online resource (120 p.) 225 1 $aLecture notes in mathematics (Springer-Verlag) ;$v1915 300 $a"ISSN electronic edition 1617-9692." 311 $a3-540-73509-7 320 $aIncludes bibliographical references and index. 327 $aGraph Laplacians -- Eigenfunctions and Nodal Domains -- Nodal Domain Theorems for Special Graph Classes -- Computational Experiments -- Faber-Krahn Type Inequalities. 330 $aEigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus they may seem a surprising topic for a book. The authors propose two motivations for this new LNM volume: (1) There are fascinating subtle differences between the properties of solutions of Schrödinger equations on manifolds on the one hand, and their discrete analogs on graphs. (2) "Geometric" properties of (cost) functions defined on the vertex sets of graphs are of practical interest for heuristic optimization algorithms. The observation that the cost functions of quite a few of the well-studied combinatorial optimization problems are eigenvectors of associated graph Laplacians has prompted the investigation of such eigenvectors. The volume investigates the structure of eigenvectors and looks at the number of their sign graphs ("nodal domains"), Perron components, graphs with extremal properties with respect to eigenvectors. The Rayleigh quotient and rearrangement of graphs form the main methodology. 410 0$aLecture notes in mathematics (Springer-Verlag) ;$v1915. 606 $aEigenvectors 615 0$aEigenvectors. 676 $a512.9434 700 $aB?y?kog?lu$b Tu?rker$0312250 702 $aLeydold$b Josef 702 $aStadler$b Peter F.$f1965- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466505103316 996 $aLaplacian eigenvectors of graphs$91019628 997 $aUNISA