3 citations · 9 across the 14 of their papers we have counts for
5 papers · 1 filter
Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
Xiaolong Fan, Maoguo Gong, Yue Wu +2
Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains. However, constructing an…
Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach
Xiaolong Fan, Maoguo Gong, Yue Wu +3
The empirical studies of Graph Neural Networks (GNNs) broadly take the original node feature and adjacency relationship as singleview input, ignoring the rich information of multip…
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Maoguo Gong, Yuan Gao, Yue Wu +1
Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire o…
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
Xiaolong Fan, Maoguo Gong, Yue Wu +1
Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture inf…
Multi-Party Dual Learning
Maoguo Gong, Yuan Gao, Yu Xie +3
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed partie…