Search Results for author:
Found 6 papers, 2 papers with code
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms.
Probabilistically Rewired Message-Passing Neural Networks
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis
Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security.
Influence-Based Mini-Batching for Graph Neural Networks
Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches.
Ordered Subgraph Aggregation Networks
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.
Locality-Based Mini Batching for Graph Neural Networks
Training graph neural networks on large graphs is challenging since there is no clear way of how to extract mini batches from connected data.
