👉 Neighborhood Fluid is a graph neural network architecture designed to efficiently model the relationships between nodes in a graph by leveraging the concept of "neighbors." It operates on the principle that nodes within close proximity to each other in a graph tend to share similar features or behaviors. Neighborhood Fluid achieves this by dynamically aggregating information from a node's immediate neighbors, allowing it to capture both local and global structural information. This approach enables the model to learn rich representations that account for the intricate dependencies within the graph, making it particularly effective for tasks like node classification, link prediction, and graph classification where understanding the context of each node is crucial.
