#temporal graphs

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4 papers match

cs.AI2026

Dynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators

Yan Kong

The paper introduces Dynamic Spectral Filtering (DSF), a method that lets the graph propagation operator evolve over time using Chebyshev polynomial filters with time‑dependent coe…

#temporal graphs#graph neural networks#spectral filtering#link prediction
math.CO2026

A Graph Minors Approach to Temporal Sequences

Johannes Carmesin, Will J. Turner

The paper develops a graph‑minor based structural theory for simultaneous embeddability of temporal graph sequences, classifying 2‑connected sequences into five obstruction types a…

#temporal graphs#simultaneous embedding#graph minors#algorithmic complexity
cs.LG2026

What Do Temporal Graph Learning Models Learn?

Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier

The paper investigates which structural and temporal properties of graphs are actually captured by state‑of‑the‑art temporal graph learning models, using systematic tests on synthe…

#temporal graphs#graph representation learning#model interpretability#benchmark evaluation
cs.LG2026

Explaining Temporal Graph Neural Networks via Feature-induced Information Flow

Ping Xiong, Thomas Schnake, Klaus-Robert Müller +1

The paper introduces an attribution method that explains temporal graph neural networks by quantifying information flow through both event embeddings and event-induced variables, i…

#temporal graphs#explainability#graph neural networks#information flow