Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data

no code implementations • 19 Apr 2024 • Zhenzhong Wang, Qingyuan Zeng, WanYu Lin, Min Jiang, Kay Chen Tan

While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.

Node Classification Self-Supervised Learning

Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks

no code implementations • 8 Jan 2021 • Zhenzhong Wang, Haokai Hong, Kai Ye, Min Jiang, Kay Chen Tan

However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.

Evolutionary Algorithms Generative Adversarial Network +1

Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning

no code implementations • 19 Oct 2019 • Zhenzhong Wang, Min Jiang, Xing Gao, Liang Feng, Weizhen Hu, Kay Chen Tan

In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.

regression Transfer Learning