9 papers · 1 filter
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…
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…
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…