👉 The Neighbor Fluid is a concept in graph neural networks (GNNs) that generalizes the idea of neighborhood aggregation by considering not just directly connected nodes but also their neighbors' neighbors, or "third-degree" neighbors. This approach allows the model to capture more complex and higher-level structural information within the graph, enhancing its ability to learn meaningful representations. By incorporating this extended neighborhood, the Neighbor Fluid can better understand the context and relationships between nodes at various levels of abstraction, leading to improved performance in tasks like node classification, link prediction, and graph classification.
