000			03645nam a22002537a 4500
005			20230920110437.0
008			230920b |||||||| |||| 00| 0 eng d
020			_a9781680831306
028			_a: Zafaa Books & Distributors _b: 313/56F, 49A, Anand Nagar, Inderlok, delhi _c: ZBD0148 _d: 15/09/2023
082			_a005.1 TEN
100			_aTeng, Shang-Hua.
245			_a Scalable algorithms for data and network analysis _c/ Shang-Hua Teng.
260			_a, Massachusetts _bNow Publishers _c, 2016.
300			_axi,278p. _c: 24cm.
365			_b9546
505			_a 1. Scalable algorithms 1.1 Challenges of massive data 1.2 The scalability of algorithms 1.3 Complexity Class S 1.4 Scalable reduction and algorithmic primitives 1.5 Article organization 2. Networks and data 2.1 Weighted graphs and affinity networks 2.2 Possible sources of affinities 2.3 Beyond graph models for social/information networks 2.4 Basic problems in data and network analysis 2.5 Sparse networks and sparse matrices 3. Significant nodes: sampling making data smaller 3.1 Personalized PageRank matrix 3.2 Multi-precision annealing for significant PageRank 3.3 Local approximation of personalized PageRank 3.4 Multi-precision sampling 3.5 Significant-PageRank identification 4. Clustering: local exploration of networks 4.1 Local algorithms for network analysis 4.2 Local clustering and random walks 4.3 Performance analysis of local clustering 4.4 Scalable local computation of personalized PageRank 4.5 Discussions: local exploration of networks 4.6 Interplay between dynamic processes and networks 4.7 Cheeger's inequality and its parameterization 5. Partitioning: geometric techniques for data analysis 5.1 Centerpoints and regression depth 5.2 Scalable algorithms for centerpoints 5.3 Geometric separators 5.4 Dimension reduction: random vs spectral 5.5 Scalable geometric divide-and-conquer 5.6 Graph partitioning: vertex and edge separators 5.7 Multiway partition of network and geometric data 5.8 Spectral graph partitioning: the geometry of a graph 6. Sparsification: making networks simpler 6.1 Spectral similarity of graphs 6.2 Some basic properties of spectrally similar networks 6.3 Spectral graph sparsification 6.4 Graph inequalities and low-stretch spanning trees 6.5 Edge centrality, sampling, and spectral approximation 6.6 Scalable dense-matrix computation via sparsification 6.7 PageRank completion of networks 7. Electrical flows: Laplacian paradigm for network analysis 7.1 SDD primitive and its scalability 7.2 Electrical flow and Laplacian linear systems 7.3 Spectral approximation and spectral partitioning 7.4 Learning from labeled network data 7.5 Sampling from Gaussian Markov random fields 7.6 Scalable Newton's method via spectral sparsification 7.7 Laplacian paradigm 8. Remarks and discussions 8.1 Beyond graph-based network models 8.2 Discussions: a family of scaling-invariant clusterability 8.3 Data clustering: intuition versus ground truth 8.4 Behaviors of algorithms: beyond worst-case analysis 8.5 Final remarks Acknowledgements References
520			_aSurveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning. They also include spectral graph-theoretical methods, such as are used for computing electrical flows and sampling from Gaussian Markov random fields.
650			_aAlgorithmes
650			_aComputational geometry
650			_aData Mining
650			_aGraphical models
700			_aShang-Hua Teng.
942			_cBOOK
999			_c60855 _d60855