Dynamic graph neural networks

insight - Dynamic graph neural networks

Robust Knowledge Adaptation for Dynamic Graph Neural Networks: Reinforcement Learning-based Approach for Selective Node Updates

The core message of this paper is to propose a reinforcement learning-based framework, called Ada-DyGNN, that can adaptively determine which nodes should be updated when new connections are established in dynamic graphs. This is in contrast to previous approaches that blindly update the embeddings of all neighboring nodes, which can lead to performance degradation when the new connections contain noisy or outdated information.

Robust Dynamic Graph Representation Learning with Information Bottleneck

The core message of this paper is to propose the Dynamic Graph Information Bottleneck (DGIB) framework, which learns robust and discriminative representations for dynamic graphs by leveraging the Information Bottleneck (IB) principle. DGIB aims to satisfy the Minimal-Sufficient-Consensual (MSC) Condition, which encourages the learned representations to be minimal, sufficient, and consensual across graph snapshots for robust future link prediction.

About

Products | Resources

Insights