21 citations · 30 across the 8 of their papers we have counts for
8 papers
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closel…
Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset
Felix Burr, Marcel Hoffmann, Ansgar Scherp
Despite the ample availability of graph data, obtaining vertex labels is a tedious and expensive task. Therefore, it is desirable to learn from a few labeled vertices only. Existin…
Edge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs without Message Passing
Matthias Kohn, Marcel Hoffmann, Ansgar Scherp
Message Passing Neural Networks (MPNNs) have demonstrated remarkable success in node classification on homophilic graphs. It has been shown that they do not solely rely on homophil…
Lifelong Graph Learning for Graph Summarization
Jonatan Frank, Marcel Hoffmann, Nicolas Lell +2
Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph su…
POWN: Prototypical Open-World Node Classification
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
We consider the problem of \textit{true} open-world semi-supervised node classification, in which nodes in a graph either belong to known or new classes, with the latter not presen…
Open-World Lifelong Graph Learning
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
We study the problem of lifelong graph learning in an open-world scenario, where a model needs to deal with new tasks and potentially unknown classes. We utilize Out-of-Distributio…
Fine-Tuning Language Models for Scientific Writing Support
Justin Mücke, Daria Waldow, Luise Metzger +4
We support scientific writers in determining whether a written sentence is scientific, to which section it belongs, and suggest paraphrasings to improve the sentence. Firstly, we p…
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
Lukas Galke, Iacopo Vagliano, Benedikt Franke +3
Lifelong graph learning deals with the problem of continually adapting graph neural network (GNN) models to changes in evolving graphs. We address two critical challenges of lifelo…