Hop Neighbor

"Hop neighbor" research focuses on optimizing information aggregation in graph-structured data and distributed systems by strategically considering the connections between nodes at varying distances (hops). Current research emphasizes improving the robustness and efficiency of algorithms like Graph Neural Networks (GNNs) and Federated Learning (FL) by selectively incorporating information from near and far neighbors, addressing issues like over-squashing and data heterogeneity. This work is significant for enhancing the performance and scalability of machine learning models on complex datasets and improving the resilience of distributed systems to noise and attacks.