Computational Data and Social Networks (eBook, PDF)

8th International Conference, CSoNet 2019, Ho Chi Minh City, Vietnam, November 18-20, 2019, Proceedings
Redaktion: Tagarelli, Andrea; Tong, Hanghang

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019.

The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions. The papers appear under the following topical headings: Combinatorial Optimization and Learning; In¿uence Modeling, Propagation, and Maximization; NLP and A¿ective Computing; Computational Methods for Social Good; and User Pröling and Behavior Modeling.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 372
• Erscheinungstermin: 11. November 2019
• Englisch
• ISBN-13: 9783030349806
• Artikelnr.: 58254446

Inhaltsangabe

Combinatorial Optimization and Learning.- A Probabilistic Divide and Conquer Algorithm for the Minimum Tollbooth Problem.- Distributed Core Decomposition in Probabilistic Graphs.- Sampled Fictitious Play on Networks.- Outlier Detection Forest for Large-scale Categorical Data Sets.- Neural Networks with Multidimensional Cross-Entropy Loss Functions.- A RVND+ILS metaheuristic to Solve the Delivery Man Problem with Time Windows.- Star2vec: from subspace embedding to whole-space embedding for intelligent recommendation system (Extended Abstract).- Approximation algorithm for the squared metric soft capacitated facility location problem (Extended Abstract).- An E cient Algorithm for the k-Dominating Set Problem on Very Large-Scale Networks (Extended Abstract).- Strategy-proof cost-sharing mechanism design for a generalized cover-sets problem (Extended Abstract).- In uence Modeling, Propagation, and Maximization.- Hybrid Centrality Measures for Service Coverage Problem.-Hop-based Sketch for Large-scale In uence Analysis.- Profit Maximization under Group In uence Model in Social Networks.- Importance Sample-based Approximation Algorithm for Cost-aware Targeted Viral Marketing.- Location-Based Competitive In uence Maximization in Social Networks.- Cascade of Edge Activation in Networks.- Reinforcement Learning in Information Cascades Based on Dynamic User Behavior.- Predicting New Adopters via Socially-Aware Neural Graph Collaborative Filtering.- Who Watches What: forecasting viewership for the top 100 TV networks.- NLP and A ective Computing.- PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis.- One-document Training For Vietnamese Sentiment Analysis.- Assessing the readability of Vietnamese texts through comparison.- Learning Representations for Vietnamese Sentence Classification (Extended Abstract).- Exploring Machine Translation on the Chinese-Vietnamese Language Pair.- Computational Methods for Social Good.- Towards an Aspect-based Ranking Model for Clinical Trial Search.- Scalable Self-Taught Deep-embedded Learning Framework for Drug Abuse Spatial Behaviors Detection.- Limiting the Neighborhood: De-Small-World Network for Outbreak Prevention.- Online Community Con ict Decomposition with Pseudo Spatial Permutation.- Attribute-Enhanced De-anonymization of Online Social Networks.- Subgraph-based Adversarial Examples Against Graph-based IoT Malware Detection Systems.- Incorporating Content beyond Text: A High Reliable Twitter-based Disaster Information System.- Reduced-Bias Co-Trained Ensembles for Weakly Supervised Cyberbullying Detection.- A Novel Privacy-Preserving Socio-Technical Platform for Detecting Cyber Abuse (Extended Abstract).- Projection-based Coverage Algorithms in 3D Camera Sensor Networks for Indoor Objective Tracking.- User Profiling and Behavior Modeling.- Enhancing Collaborative Filtering with Multi-label Classification.- Link Prediction on Dynamic Heterogeneous Information Networks.- Information Network Cascading and Network Re-construction with Bounded Rational User Behaviors.- Gender Prediction through Synthetic Resampling of User Profiles using SeqGANs.