Multihop Reconstruction for Generalized Zero-Shot Node Classification

Jialong Wang, Zheng Wang, Zhiguo Gong

Abstract: Graphs in the real world keep evolving with the integration of new nodes, and it is often infeasible to manually label all the new nodes promptly. In this case, graph learning algorithms can come in handy and perform classification on these newly emerging nodes. Typically, if unseen classes exist (i.e., no training samples from these classes), one can perform zero-shot learning (ZSL) or generalized ZSL (GZSL). During testing, ZSL aims to classify samples within unseen classes, whereas GZSL aims to classify samples within both seen and unseen classes, which is even more challenging. In our previous work, we proposed a decomposed graph prototype network (DGPN) to decompose the graph convolution operation for handling the zero-shot node classification (ZNC) problem. However, DGPN is not well-suited for the generalized ZNC (GZNC) problem. To this end, in this article, we propose a novel graph generative model, multihop reconstruction graph autoencoder (MHR-GAE). Unlike DGPN, MHR-GAE utilizes a multihop encoder with class semantic descriptions (CSDs) (as condition signals) to reconstruct the information and generate nodes of unseen classes. Thus, it can handle both the ZNC and GZNC problems and obtain competitive performance. We evaluate our model on real-world datasets, and the experimental results demonstrate that MHR-GAE outperforms other baseline methods.

External IDs:dblp:journals/tnn/WangWG25