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Research Frontiers in Graph Neural Networks and Relational Learning

Exploration of neural network architectures designed to process graph-structured data and learn from complex relational patterns.

Heterophilic Graph Learning Beyond Homophily Assumptions
Dynamic Temporal Graphs and Evolving Relational Structures
Expressive Power Limits in Message Passing Neural Networks
Graph Neural Networks on Sparse and Incomplete Relations
Interpretability of Learned Relational Embeddings
Symmetry and Equivariance in Graph Representation Learning
Scalable Graph Learning for Billion-Node Networks
Knowledge Graph Completion via Neural Relational Reasoning
Adversarial Robustness in Graph Neural Networks
Multi-Modal Relational Learning Across Heterogeneous Data

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