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mohaisen david (curatore); jin ruoming (curatore) - computational data and social networks

Computational Data and Social Networks 10th International Conference, CSoNet 2021, Virtual Event, November 15–17, 2021, Proceedings

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 12/2021
Edizione: 1st ed. 2021





Trama

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.

 





Sommario

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.










Altre Informazioni

ISBN:

9783030914332

Condizione: Nuovo
Collana: Lecture Notes in Computer Science
Dimensioni: 235 x 155 mm
Formato: Brossura
Illustration Notes:XII, 390 p. 97 illus., 81 illus. in color.
Pagine Arabe: 390
Pagine Romane: xii


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