IEEE

A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Abstract: Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading to ...
GitHub

Neural QAOA²: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization

The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-and-conquer frameworks like QAOA 2 address ...
IEEE

Learning Dynamic Graph Embeddings With Neural Controlled Differential Equations

Abstract: This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynamics, and ...