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

Three temporal graph datasets for node classification under distribution shift. DBLP-Easy and DBLP-Hard are citation graph datasets. PharmaBio is a collaboration graph dataset. Vertices are scientific publications, edges are either citations (DBLP) or at-least-one-common-author relationships (PharmaBio). The task is to classify the vertices of the graph into the respective conference/journal venues (DBLP) or journal categories (PharmaBio). In the DBLP datasets, new classes may appear over time. Each dataset follows the structure: - adjlist.txt -- the graph structure encoded as adjacency lists: in each row, the first entry is the source vertex, the remaining entries are adjacent vertices - X.npy -- numpy serialized format for node features indexed by node id corresponding to adjlist.txt - y.npy -- numpy serialized format for node labels indexed by node id corresponding to adjlist.txt - t.npy -- numpy serialized format for time steps indexed by node id corresponding to adjlist.txt A paper describing our incremental training and evaluation framework is published in IJCNN 2021 (Pre-print on arXiv: https://arxiv.org/abs/2006.14422). If you use these datasets in your research, please cite the corresponding paper: <pre><code>@inproceedings{galke2021lifelong, author={Galke, Lukas and Franke, Benedikt and Zielke, Tobias and Scherp, Ansgar}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={Lifelong Learning of Graph Neural Networks for Open-World Node Classification}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/IJCNN52387.2021.9533412} }</code></pre> <br>