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Titolo: | 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 and Label 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 |
Opac: | Controlla la disponibilità qui |