تقرير
Block Modeling-Guided Graph Convolutional Neural Networks
| العنوان: | Block Modeling-Guided Graph Convolutional Neural Networks |
|---|---|
| المؤلفون: | He, Dongxiao, Liang, Chundong, Liu, Huixin, Wen, Mingxiang, Jiao, Pengfei, Feng, Zhiyong |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Social and Information Networks |
| الوصف: | Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modeling into the framework of GCN so that it can realize "block-guided classified aggregation", and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modeling into the aggregation process, GCN is able to aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets. Comment: Accepted by Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2112.13507Test |
| رقم الانضمام: | edsarx.2112.13507 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |