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

<p>Three temporal graph datasets for node classification under distribution shift.</p> <p>DBLP-Easy and DBLP-Hard are citation graph datasets. PharmaBio is a collaboration graph dataset.</p> <p>Vertices are scientific publications, edges are either citations (DBLP) or at-least-one-common-author relationships (PharmaBio).</p> <p>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.</p> <p>Each dataset follows the structure:</p> <p>- 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</p> <p>- X.npy -- numpy serialized format for node features indexed by node id corresponding to adjlist.txt</p> <p>- y.npy -- numpy serialized format for node labels indexed by node id corresponding to adjlist.txt</p> <p>- t.npy -- numpy serialized format for time steps indexed by node id corresponding to adjlist.txt</p> <p>A paper describing our incremental training and evaluation framework is published in IJCNN 2021 (Pre-print on arXiv:&nbsp;<a href="https://arxiv.org/abs/2006.14422">https://arxiv.org/abs/2006.14422</a>).</p> <p>If you use these datasets in your research, please cite the corresponding paper:</p> <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> <p><br> &nbsp;</p>