Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes

1 code implementation • 20 Dec 2021 • Lukas Galke, Iacopo Vagliano, Benedikt Franke, Tobias Zielke, Marcel Hoffmann, Ansgar Scherp

The combination of these two challenges is particularly relevant since newly emerging classes typically resemble only a tiny fraction of the data, adding to the already skewed class distribution.

Graph Attention Graph Learning +2

Online Learning of Graph Neural Networks: When Can Data Be Permanently Deleted

no code implementations • 1 Jan 2021 • Lukas Paul Achatius Galke, Benedikt Franke, Tobias Zielke, Ansgar Scherp

In most cases, i. e., 15 out 18 experiments, we even observe that a temporal window of size 1 is sufficient to retain at least 90%.

Lifelong Learning of Graph Neural Networks for Open-World Node Classification

1 code implementation • 25 Jun 2020 • Lukas Galke, Benedikt Franke, Tobias Zielke, Ansgar Scherp

Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data such as node classification.

Node Classification