2 citations · 7 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
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,…