Non-negative Weighted DAG Structure Learning
Speaker: Samuel Rey Escudero
Location: 0.W100 Turing Room, Building 28
Time: 15:00 - 15:45
Zoom: link
Joint Recovery of Simplicial Complexes via Binary Linear Programming
Speaker: Varun Sarathchandran
Location: 0.W100 Turing Room, Building 28
Time: 15:45 - 16:05
Zoom: link
NodePro: An Instance-Level Profiling Framework for Graph-Structured Data
Speaker: Tianqi Zhao
Location: 0.W100 Turing Room, Building 28
Time: 16:05 - 16:25
Zoom: link
Abstract:
Graph machine learning models often achieve similar overall performance yet behave differently at the node level—failing on different subsets of nodes with varying reliability. Standard evaluation metrics such as accuracy obscure these fine-grained differences, making it difficult to diagnose when and where models fail. We introduce NodePro, a node profiling framework that enables fine-grained diagnosis of model behavior by assigning interpretable profile scores to individual nodes. These scores combine data-centric signals—such as feature dissimilarity, label uncertainty, and structural ambiguity—with model-centric measures of prediction confidence and consistency during training. By aligning model behavior with these profiles, NodePro reveals systematic differences between models, even when aggregate metrics are indistinguishable. We show that node profiles generalize to unseen nodes, supporting prediction reliability without ground-truth labels. Finally, we demonstrate the utility of NodePro in identifying semantically inconsistent or corrupted nodes in a structured knowledge graph, illustrating its effectiveness in real-world settings.