2 citations · 3 across the 12 of their papers we have counts for
8 papers · 1 filter
Can Graph Learning Learn Circuits?
Chester Tan, Moritz Lampert, Courtney Maynard +3
Circuit localization is a mechanistic interpretability task whose goal is to identify a sparse subgraph of a transformer's computation graph sufficient to reproduce a particular be…
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…
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
Franziska Heeg, Jonas Sauer, Petra Mutzel +1
An important characteristic of temporal graphs is how the directed arrow of time influences their causal topology, i.e., which nodes can possibly influence each other causally via…
Deep Graph Learning will stall without Network Science
Christopher Blöcker, Martin Rosvall, Ingo Scholtes +1
Deep graph learning focuses on flexible and generalizable models that learn patterns in an automated fashion. Network science focuses on models and measures revealing the organizat…
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length
Jan von Pichowski, Christopher Blöcker, Ingo Scholtes
Graph pooling compresses graphs and summarises their topological properties and features in a vectorial representation. It is an essential part of deep graph representation learnin…
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…
The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural Networks
Moritz Lampert, Ingo Scholtes
Many Graph Neural Networks (GNNs) add self-loops to a graph to include feature information about a node itself at each layer. However, if the GNN consists of more than one layer, t…
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
Franziska Heeg, Ingo Scholtes
Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betwe…