25 citations · 179 across the 54 of their papers we have counts for
18 papers · 1 filter
Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning
Jonatan Frank, Jonathan Frank, David Richerby +1
Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings. Chimaera integrates mixtur…
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…
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
Nicolas Lell, Ansgar Scherp
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V a…
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…
A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification
Younes Yousef, Lukas Galke, Ansgar Scherp
Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the…
LossVal: Efficient Data Valuation for Neural Networks
Tim Wibiral, Mohamed Karim Belaid, Maximilian Rabus +1
Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is co…
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…
HyperAggregation: Aggregating over Graph Edges with Hypernetworks
Nicolas Lell, Ansgar Scherp
HyperAggregation is a hypernetwork-based aggregation function for Graph Neural Networks. It uses a hypernetwork to dynamically generate weights in the size of the current neighborh…
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…
The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
Nicolas Lell, Ansgar Scherp
When training a Neural Network, it is optimized using the available training data with the hope that it generalizes well to new or unseen testing data. At the same absolute value,…
Graph Summarization with Graph Neural Networks
Maximilian Blasi, Manuel Freudenreich, Johannes Horvath +2
The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph…
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…
rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm
Fabian Singhofer, Aygul Garifullina, Mathias Kern +1
Traditional approaches for data anonymization consider relational data and textual data independently. We propose rx-anon, an anonymization approach for heterogeneous semi-structur…
A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes
Ishwar Venugopal, Jessica Töllich, Michael Fairbank +1
Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better p…
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
Lukas Galke, Benedikt Franke, Tobias Zielke +1
Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data such as node classification. However, real-world graphs are often evolv…
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
Lukas Galke, Iacopo Vagliano, Ansgar Scherp
Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising,…