Search Results - Parameterized Complexity Algorithms Graph Algorithms Strong Triadic Closure Bayesian Networks*

Parameterized Algorithmics for Graph-Based Data Analysis ; Parametrisierte Algorithmik für graphbasierte Datenanalyse

Authors: Grüttemeier, Niels Komusiewicz, Christian (Prof. Dr.)

Contributors: Grüttemeier, Niels Komusiewicz, Christian (Prof. Dr.)

Subject Terms: Parameterized Complexity Algorithms Graph Algorithms Strong Triadic Closure Bayesian Networks, Parametrisierte Komplexität, Algorithmik

Time: 510

File Description: application/pdf

Relation: diss/z2022/0114; https://doi.org/10.17192/z2022.0114

Availability: https://open.uni-marburg.de/handle/10.17192/z2022.0114
https://nbn-resolving.org/urn:nbn:de:hebis:04-z2022-01143
https://doi.org/10.17192/z2022.0114

Mathematical Frameworks for Network Dynamics: A Six-Pillar Survey for Analysis, Control, and Inference.

Authors: Volchenkov, Dimitri

Source: Mathematics (2227-7390); Jul2025, Vol. 13 Issue 13, p2116, 74p

Subject Terms: SPECTRAL theory, DYNAMICAL systems, MULTILAYERS, MEAN field theory, MATHEMATICAL models, CONTROL theory (Engineering), INFERENTIAL statistics, TIME-varying networks

Graph Neural Networks in IoT: A Survey.

Authors: GUIMIN DONG MINGYUE TANG ZHIYUAN WANG et al.

Source: ACM Transactions on Sensor Networks; May2023, Vol. 19 Issue 2, p1-50, 50p

Subject Terms: DEEP learning, CONVOLUTIONAL neural networks, RECURRENT neural networks, ARTIFICIAL neural networks, INTERNET of things, TELECOMMUNICATION, MACHINE learning

An Efficient Bet-GCN Approach for Link Prediction.

Authors: Saxena, Rahul Patil, Spandan Pankaj Verma, Atul Kumar et al.

Source: International Journal of Interactive Multimedia & Artificial Intelligence; Mar2023, Vol. 8 Issue 1, p38-52, 15p

Subject Terms: GENERALIZABILITY theory, SOCIAL network analysis, BIOLOGICAL networks, PROTEIN-protein interactions, POLYMER networks, PREDICTION models, FORECASTING

Company/Entity: AMAZON.COM Inc.

Advances in Graph Neural Networks.

Authors: Chuan Shi Xiao Wang Cheng Yang et al.

Source: Synthesis Lectures on Data Mining & Knowledge Discovery; 2023, piii-200, 206p

What Are Higher-Order Networks?

Authors: Bick, Christian Gross, Elizabeth Harrington, Heather A. et al.

Source: SIAM Review; Sep2023, Vol. 65 Issue 3, p686-731, 46p

Random Graph Modeling: A Survey of the Concepts.

Authors: DROBYSHEVSKIY, MIKHAIL TURDAKOV, DENIS

Source: ACM Computing Surveys; Nov2020, Vol. 52 Issue 6, p1-36, 36p, 1 Illustration

Subject Terms: RANDOM graphs, BIOLOGICAL networks, SOCIAL networks, HYPERBOLIC geometry

State-Dependent Kernel Selection for Conditional Sampling of Graphs.

Authors: Scott, James A. Gandy, Axel

Source: Journal of Computational & Graphical Statistics; Oct-Dec2020, Vol. 29 Issue 4, p847-858, 12p

Subject Terms: MONTE Carlo method, KERNEL (Mathematics), CONTINGENCY tables, MARKOV chain Monte Carlo, WEIGHTED graphs

Combinatorial Algorithms

Authors: Sun-Yuan Hsieh Ling-Ju Hung Chia-Wei Lee

Resource Type: eBook.

Subjects: Computer science—Mathematics, Discrete mathematics, Computer engineering, Computer networks, Algorithms, Data structures (Computer science), Information theory, Computer graphics, Numerical analysis

Categories: COMPUTERS / Computer Science, COMPUTERS / Software Development & Engineering / Computer Graphics, COMPUTERS / Database Administration & Management, COMPUTERS / Information Theory, COMPUTERS / Hardware / General, MATHEMATICS / Discrete Mathematics, MATHEMATICS / Numerical Analysis

Network representation learning: a systematic literature review.

Authors: Li, Bentian Pi, Dechang

Source: Neural Computing & Applications; Nov2020, Vol. 32 Issue 21, p16647-16679, 33p

Subject Terms: VECTOR topology, VECTOR spaces, MACHINE learning

Finding a Place for Networks in Archaeology.

Authors: Peeples, Matthew A.

Source: Journal of Archaeological Research; Dec2019, Vol. 27 Issue 4, p451-499, 49p

Subject Terms: SOCIAL network analysis, SOCIAL groups, SOCIAL networks, GRAPH theory, HISTORY of archaeology, ARCHAEOLOGY

45 th Annual North American Meeting of the Society for Medical Decision Making, Philadelphia, Pennsylvania, United States, 22-25 October 2023.

Source: Medical Decision Making; Apr2024, Vol. 44 Issue 3, pNP1-NP375, 375p

Network Embedding: Theories, Methods, and Applications.

Authors: Cheng Yang Zhiyuan Liu Cunchao Tu et al.

Source: Synthesis Lectures on Artificial Intelligence & Machine Learning; 2021, Vol. 15 Issue 2, pix-220, 234p

Confidence sets for network structure.

Authors: Airoldi, Edoardo M. Choi, David S. Wolfe, Patrick J.

Source: Statistical Analysis & Data Mining; Oct2011, Vol. 4 Issue 5, p461-469, 9p

Subject Terms: ADOLESCENT health, RANDOM graphs, LATENT variables, BERNOULLI numbers, ANALYSIS of covariance

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids.

Authors: Bukowski, Brandon C. Keil, Frerich J. Ravikovitch, Peter I. et al.

Source: Adsorption; Jul2021, Vol. 27 Issue 5, p683-760, 78p

Social network analysis: A methodological introduction.

Authors: Butts, Carter T.

Source: Asian Journal of Social Psychology; Mar2008, Vol. 11 Issue 1, p13-41, 29p

Subject Terms: NETWORK analysis (Planning), SOCIAL networks, INTERPERSONAL relations, SOCIAL groups, OPERATIONS research, GRAPH theory, PRODUCTION scheduling, METHODOLOGY, SOCIOLOGY

Semantic Mining of Social Networks.

Authors: Jie Tang Juanzi Li

Source: Synthesis Lectures on the Semantic Web: Theory & Technology; 2015, Vol. 5 Issue 2, pi-193, 205p

Link prediction in social networks: the state-of-the-art.

Alternate Title: 社交网络中的链接预测综述. (Chinese)

Authors: Wang, Peng Xu, BaoWen Wu, YuRong et al.

Source: SCIENCE CHINA Information Sciences; Jan2015, Vol. 58 Issue 1, p1-38, 38p

HOMOPHILY AND TRANSITIVITY IN DYNAMIC NETWORK FORMATION.

Authors: Graham, Bryan S.

Source: NBER Working Papers; 4/25/2016, preceding p1-37, 39p

40th Annual Meeting of the Society for Medical Decision Making Montréal, Québec, Canada, October 13–17, 2018.

Source: Medical Decision Making; Jan2019, Vol. 39 Issue 1, pE1-E343, 343p