314 citations · 444 across the 20 of their papers we have counts for
7 papers · 1 filter
Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters
Lisi Qarkaxhija, Ingo Scholtes
Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interaction patterns can provide compe…
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs
Jan von Pichowski, Vincenzo Perri, Lisi Qarkaxhija +1
The modelling of temporal patterns in dynamic graphs is an important current research issue in the development of time-aware GNNs. Whether or not a specific sequence of events in a…
Link Prediction with Untrained Message Passing Layers
Lisi Qarkaxhija, Anatol E. Wegner, Ingo Scholtes
Message passing neural networks (MPNNs) operate on graphs by exchanging information between neigbouring nodes. MPNNs have been successfully applied to various node-, edge-, and gra…
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,…
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs
Lisi Qarkaxhija, Vincenzo Perri, Ingo Scholtes
We introduce De Bruijn Graph Neural Networks (DBGNNs), a novel time-aware graph neural network architecture for time-resolved data on dynamic graphs. Our approach accounts for temp…
Learning the Markov order of paths in a network
Luka V. Petrović, Ingo Scholtes
We study the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e. sequences of variable lengths where transitions between states ar…
Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
Christoph Gote, Giona Casiraghi, Frank Schweitzer +1
We propose a novel sequence prediction method for sequential data capturing node traversals in graphs. Our method builds on a statistical modelling framework that combines multiple…