In addition to the adjacency matrix and node features, other types of temporal or structural information that could be leveraged to enhance generalization estimation in evolving graphs include:
Temporal Dynamics: Incorporating temporal information such as timestamps of node/edge additions or modifications can provide valuable insights into the evolution of the graph over time. By analyzing the temporal patterns in the graph, SMART can adapt its estimation process to capture the temporal dynamics and improve generalization performance.
Community Structure: Utilizing community detection algorithms to identify clusters or communities within the graph can help in understanding the structural organization of the graph. By considering the community structure in the estimation process, SMART can better capture the underlying relationships and interactions between nodes.
Graph Topology Changes: Monitoring changes in the graph's topology, such as the formation of new subgraphs or the dissolution of existing connections, can provide important cues for estimating generalization performance. By tracking and analyzing these topology changes, SMART can adjust its estimation strategy to account for evolving graph structures effectively.