no code implementations • 14 Jul 2022 • Weijian Chen, Yixin Cao, Fuli Feng, Xiangnan He, Yongdong Zhang
On the one hand, their performance will dramatically degrade along with the increasing sparsity of KGs.
1 code implementation • 5 Mar 2021 • Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, Xiangnan He
Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.
1 code implementation • 11 Sep 2020 • Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang
In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical.
1 code implementation • 30 Jan 2020 • Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.