LEADER 04173nam 22006375 450 001 9910568239603321 005 20251113175324.0 010 $a3-030-97568-1 024 7 $a10.1007/978-3-030-97568-5 035 $a(MiAaPQ)EBC6977367 035 $a(Au-PeEL)EBL6977367 035 $a(CKB)22046197700041 035 $a(PPN)269155244 035 $a(OCoLC)1315540704 035 $a(DE-He213)978-3-030-97568-5 035 $a(EXLCZ)9922046197700041 100 $a20220506d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCohesive Subgraph Search Over Large Heterogeneous Information Networks /$fby Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (86 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Fang, Yixiang Cohesive Subgraph Search over Large Heterogeneous Information Networks Cham : Springer International Publishing AG,c2022 9783030975678 320 $aIncludes bibliographical references (pages 65-74). 327 $aIntroduction -- Preliminaries -- CSS on Bipartite Networks -- CSS on Other General HINs -- Comparison Analysis -- Related Work on CSMs and solutions -- Future Work and Conclusion. 330 $aThis SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs. The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas. This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aInformation storage and retrieval systems 606 $aComputer science$xMathematics 606 $aDiscrete mathematics 606 $aGraph theory 606 $aInformation Storage and Retrieval 606 $aDiscrete Mathematics in Computer Science 606 $aGraph Theory 615 0$aInformation storage and retrieval systems. 615 0$aComputer science$xMathematics. 615 0$aDiscrete mathematics. 615 0$aGraph theory. 615 14$aInformation Storage and Retrieval. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aGraph Theory. 676 $a006.312 676 $a006.312 700 $aFang$b Yixiang$01227727 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910568239603321 996 $aCohesive subgraph search over large heterogeneous information networks$92989205 997 $aUNINA LEADER 03183nam 22006375 450 001 9910483141803321 005 20251113185150.0 010 $a3-030-64541-X 024 7 $a10.1007/978-3-030-64541-0 035 $a(CKB)4100000011704506 035 $a(DE-He213)978-3-030-64541-0 035 $a(MiAaPQ)EBC6452034 035 $a(PPN)253254280 035 $a(EXLCZ)994100000011704506 100 $a20210106d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aController Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems $eA Metaheuristic Approach /$fby Maude Josée Blondin 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (X, 101 p. 27 illus., 20 illus. in color.) 225 1 $aSpringerBriefs in Optimization,$x2191-575X 311 08$a3-030-64540-1 327 $a1 Optimization algorithms in control systems -- 2 Controller tuning by metaheuristics optimization -- 3 Case studies -- 4 Future direction and research trends. 330 $aThis book covers controller tuning techniques from conventional to new optimization methods for diverse control engineering applications. Classical controller tuning approaches are presented with real-world challenges faced in control engineering. Current developments in applying optimization techniques to controller tuning are explained. Case studies of optimization algorithms applied to controller tuning dealing with nonlinearities and limitations like the inverted pendulum and the automatic voltage regulator are presented with performance comparisons. Students and researchers in engineering and optimization interested in optimization methods for controller tuning will utilize this book to apply optimization algorithms to controller tuning, to choose the most suitable optimization algorithm for a specific application, and to develop new optimization techniques for controller tuning. 410 0$aSpringerBriefs in Optimization,$x2191-575X 606 $aMathematical optimization 606 $aCalculus of variations 606 $aControl engineering 606 $aNumerical analysis 606 $aDifferential equations 606 $aCalculus of Variations and Optimization 606 $aControl and Systems Theory 606 $aNumerical Analysis 606 $aDifferential Equations 615 0$aMathematical optimization. 615 0$aCalculus of variations. 615 0$aControl engineering. 615 0$aNumerical analysis. 615 0$aDifferential equations. 615 14$aCalculus of Variations and Optimization. 615 24$aControl and Systems Theory. 615 24$aNumerical Analysis. 615 24$aDifferential Equations. 676 $a519.4 700 $aBlondin$b Maude Jose?e$01220964 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483141803321 996 $aController tuning optimization methods for multi-constraints and nonlinear systems$92830642 997 $aUNINA