Intro
General Chairs' Preface
PC Chairs' Preface
Organization
Contents - Part II
Graphs and Networks
Improving Knowledge Graph Entity Alignment with Graph Augmentation
1 Introduction
2 Related Works
3 Preliminaries
4 Methodology
4.1 Entity-Relation Encoder
4.2 Model Training with Graph Augmentation
4.3 Alignment Inference
5 Experimental Setup
5.1 Experimental Setup
5.2 Experimental Results
6 Discussion and Conclusion
References
MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction

1 Introduction
2 Related Work
2.1 Translation-Based Approaches
2.2 Matrix Factorization-Based Approaches
2.3 Neural Network-Based Approaches
3 Methodology
3.1 Problem Formulation and Notations
3.2 Overall Architecture Design
3.3 Model Architecture
4 Experiments
4.1 Datasets
4.2 Evaluation Protocol and Metric
4.3 Hyperparameters and Baselines
4.4 Results and Discussion
4.5 Analysis
5 Conclusion and Future Work
References
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation
1 Introduction

2 Related Works
2.1 Temporal Dynamics Modeling on Graph-Structured Data
2.2 Representation Learning on Graphs with Edge Features
3 Proposed Methods
3.1 Problem Formulation
3.2 Overview of GTEA
3.3 Learning Edge Embeddings for Interaction Sequences
3.4 Representation Learning with Temporal Edge Aggregation
3.5 Model Training for Different Graph-Related Tasks
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results of Overall Performance
4.3 Experiments Analyses
5 Conclusions
References

You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network
1 Introduction
2 Related Works
3 Problem Formulation
4 Methodology
4.1 Global Topology Structure Extraction
4.2 Graph Structure Memory Augmented Representation Learning
4.3 Objective Function of GSM-GNN
5 Experiments
5.1 Datasets
5.2 Experimental Setup
5.3 Performance on Node Classification
5.4 Flexibility of GSM-GNN for Various GNNs
5.5 Ablation Study
6 Conclusion
References

UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network
1 Introduction
2 Preliminaries
2.1 Problem Statement
2.2 Motivations and Challenges
2.3 Related Work
3 Approach
3.1 Overview
3.2 1-Hop Attention Module with Attention Baseline Mechanism
3.3 Confidence Score Prediction and Training Objective
3.4 Pseudo-neighbor Augmented Graph Attention Network
4 Experiment
4.1 Settings
4.2 Results and Analysis
4.3 Ablation Study
4.4 Deterministic Settings
5 Conclusion and Future Work
References