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cs.LG2026
The Role of Node Features in Graph Pooling
Jan von Pichowski, Alžbeta Hrabošová, Ingo Scholtes +1
Graph pooling is commonly applied in graph classification, yet its empirical gains over standard WL-1 expressive GNNs are often marginal or inconsistent. We study this gap by analy…
cs.LG2026
From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning
Moritz Lampert, Christopher Blöcker, Ingo Scholtes
Dynamic link prediction is an important problem considered in many recent works that propose approaches for learning temporal edge patterns. To assess their efficacy, models are ev…
cs.LG2024
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
Christopher Blöcker, Chester Tan, Ingo Scholtes
Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science,…