Intro
General Chairs' Preface
PC Chairs' Preface
Organization
Contents - Part IV
Scientific Data
Inline Citation Classification Using Peripheral Context and Time-Evolving Augmentation*-12pt
1 Introduction
2 Related Work
3 Methodology
3.1 Cross-Text Attention
3.2 Spatial Fusion
3.3 Time Evolving Augmentation
4 Experiments
4.1 Dataset
4.2 Implementation Details
5 Baselines
6 Analysis
7 Conclusion
References
Social Network Analysis
Post-it: Augmented Reality Based Group Recommendation with Item Replacement
1 Introduction

2 Problem Formulation
3 STAR3
3.1 Interaction- and Preference-Aware Graph Attention Network
3.2 Haptic-Aware Virtual Candidate Item Generator
3.3 Social- and Haptic-Aware Recommender
3.4 Overall Objective
4 Experiments
5 Conclusion
References
Proactive Rumor Control: When Impression Counts
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Influence Model
3.2 Influence Block
3.3 Problem Definition
4 Our Framework
4.1 A Baseline
4.2 Branch-and-Bound Framework
4.3 Computing Upper Bound
4.4 Analysis of Solutions

5 Progressive Branch-and-Bound
6 Experiments
6.1 Experimental Settings
6.2 Effectiveness Test
6.3 Efficiency Test
6.4 Scalability Test
7 Conclusion
References
Spatio-Temporal Data
Generative-Contrastive-Attentive Spatial-Temporal Network for Traffic Data Imputation
1 Introduction
2 Related Work
3 Preliminaries
4 The GCASTN Model
4.1 Generative-Contrastive Self-Supervised Learning
4.2 Data Augmentation via Two-Fold Cross Random Masking
4.3 GCASTN Encoder
4.4 GCASTN Decoder
5 Experiments
5.1 Datasets and Baselines
5.2 Experimental Results

6 Conclusion
References
Road Network Representation Learning with Vehicle Trajectories*-12pt
1 Introduction
2 Problem Definition
3 TrajRNE Approach
3.1 Spatial Flow Convolution
3.2 Structural Road Encoder
3.3 TrajRNE Overview
4 Experimental Evaluation
4.1 Datasets
4.2 Baselines
4.3 Downstream Tasks and Evaluation Metrics
4.4 Experimental Settings
4.5 Performance Results
4.6 Ablation Study
4.7 Parameter Study
5 Related Work
6 Conclusion
References
MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks*-12pt

1 Introduction
2 Problem Statement
3 The MetaCitta Approach
3.1 Spatial Encoder
3.2 Temporal Encoder
3.3 Prediction
3.4 Training Procedure
4 Evaluation Setup
4.1 Datasets
4.2 Baselines
4.3 Experimental Settings
5 Evaluation
5.1 Comparison with Baselines
5.2 Ablation Study
5.3 Training Time Comparison
6 Related Work
7 Conclusion
References
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
1 Introduction
2 Preliminaries
2.1 Definitions
2.2 Problem Statement
3 Methodology
3.1 Framework Overview