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most citedThe Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural Networks

2 citations · 3 across the 12 of their papers we have counts for

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cs.LG2023★ 2 cited

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…

cs.LG2023

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…