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Research Frontiers in Graph Neural Networks and Relational Learning

Exploration of deep learning architectures for structured data represented as graphs with complex relational dependencies.

Heterophilic Learning: When Dissimilar Nodes Drive Prediction

Temporal Dynamics in Evolving Relational Systems

Explainability and Causal Inference in Graph Representations

Scalability and Sparsity in Billion-Node Networks

Message Passing Beyond Euclidean Geometry

Hypergraph Learning and Higher-Order Relational Structures

Adversarial Robustness in Graph Neural Networks

Knowledge Graph Completion via Relational Reasoning

Fairness and Bias Mitigation in Learned Graph Embeddings

Few-Shot and Zero-Shot Learning on Sparse Graphs

All Data Science PhD categories