3 citations · 3 across the 2 of their papers we have counts for
4 papers
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
Wanyu Lin, Hao Lan, Hao Wang +1
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Spe…
Towards Private Learning on Decentralized Graphs with Local Differential Privacy
Wanyu Lin, Baochun Li, Cong Wang
Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profil…
Generative Causal Explanations for Graph Neural Networks
Wanyu Lin, Hao Lan, Baochun Li
This paper presents Gem, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of…
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data
Wanyu Lin, Zhaolin Gao, Baochun Li
Graph-based semi-supervised learning has been shown to be one of the most effective approaches for classification tasks from a wide range of domains, such as image classification a…