Computational Data and Social Networks : 10th International Conference, CSoNet 2021, Virtual Event, November 15–17, 2021, Proceedings / / edited by David Mohaisen, Ruoming Jin

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• Edizione: 1st ed. 2021.
• Descrizione fisica: 1 online resource (392 pages)
• Disciplina: 006.754
• Soggetto topico: Application software; Natural language processing (Computer science); Computer networks; Data mining; Computer networks - Security measures; Computer and Information Systems Applications; Natural Language Processing (NLP); Computer Communication Networks; Data Mining and Knowledge Discovery; Mobile and Network Security
• Persona (resp. second.): JinRuoming; MohaisenDavid
• Note generali: Includes index.
• Nota di contenuto: Combinatorial Optimization and Learning -- Streaming algorithms for maximizing non-submodular functions on the integer lattice -- Causal Inference for Influence Propagation --- Identifiability of the In-dependent Cascade Model -- Streaming algorithms for Budgeted $k$-Submodular Maximization problem -- Approximation algorithms for the lower bounded correlation clustering problem -- Approximation Algorithm for Maximizing Nonnegative Weakly Mono-tonic Set Functions -- Differentially Private Submodular Maximization over Integer Lattice -- Maximizing the sum of a supermodular function and a monotone DR-submodular function subject to a knapsack constraint on the integer lattice -- Deep Learning and Applications to Complex and Social Systems -- A Framework for Accelerating Graph Convolution Networks on Massive Datasets -- AdvEdge: Optimizing Adversarial Perturbations against Interpretable Deep Learning -- Incorporating Transformer Models for Sentiment Analysis and News Classification in Khmer -- Deep Bangla Authorship Attribution using Transformer Models -- A Deep Learning Based Traffic Sign Detection for Intelligent Transportation Systems -- Detecting Hate Speech Contents Using Embedding Models -- MIC Model for Cervical Cancer Risk Factors Deep Association Analysis -- Power Grid Cascading Failure Prediction Based on Transforme -- Measurements of Insight from Data -- Security Breaches in the Healthcare Domain: A Spatiotemporal Analysis -- Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network -- Understanding the Issues Surrounding COVID-19 Vaccine Roll Out Via User Tweets -- Complex Networks Analytics -- Minimize Travel Time with Traffic Flow Density Equilibrium on Road Network -- Network based Framework to Compare Vaccination Strategies -- Groups Influence with Minimum Cost in Social Network -- Recovering communities in temporal networks using persistent edges -- Community Detection using Semilocal Topological Features andLabel Propagation Algorithm -- Twitter Analysis of Covid-19 Misinformation in Spain -- Comparing Community-aware Centrality Measures in Online Social Networks -- Two-Tier Cache-Aided Full-Duplex Content Delivery in Satellite-Terrestrial Networks -- Special Track: Fact-Checking, Fake News and Malware Detection in Online Social Networks -- Mean User-Text Agglomeration (MUTA): Practical User Representation and Visualization for Detection of Online Influence Operations -- The Role of Information Organization and Knowledge Structuring in Combatting Misinformation: A Literary Analysis -- Fake News Detection using LDA Topic Modelling and K-Nearest Neighbor Classifier -- Special Track: Information Spread in Social and Data Networks -- Summarization Algorithms for News: a Study of the Coronavirus Theme and its Impact on the News Extracting Algorithm -- Social cohesion during stay-at-home phase during the first wave of COVID-19 in Poland -- Influence and Activation Thresholds Target Set Selection within Community Structure.
• Sommario/riassunto: This book constitutes the refereed proceedings of the 10th International Conference on Computational Data and Social Networks, CSoNet 2021, which was held online during November 15-17, 2021. The conference was initially planned to take place in Montreal, Quebec, Canada, but changed to an online event due to the COVID-19 pandemic. The 24 full and 8 short papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Combinatorial optimization and learning; deep learning and applications to complex and social systems; measurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks. .
• Titolo autorizzato: Computational Data and Social Networks
• ISBN: 3-030-91434-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910512175403321
• Lo trovi qui: Univ. Federico II
• Serie: Theoretical Computer Science and General Issues, . 2512-2029 ; ; 13116