Incomplete Graph

Incomplete graphs, characterized by missing nodes, edges, or features, pose significant challenges for graph-based analyses. Current research focuses on developing robust algorithms and model architectures, such as graph neural networks (GNNs) with dual-stream learning or PU-learning, to handle this incompleteness effectively, particularly in tasks like link prediction and graph-based multi-robot path planning. These advancements are crucial for improving the reliability and accuracy of GNNs in real-world applications where complete data is rarely available, impacting fields ranging from social network analysis to knowledge graph representation learning. The development of more robust attack frameworks against incomplete graphs is also a key area of focus.