Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

no code implementations • 8 May 2024 • Rongrong Ma, Guansong Pang, Ling Chen

Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i) significantly outperforms five state-of-the-art models, and ii) offers a generic framework, in which different advanced imbalanced learning loss functions can be easily plugged in and obtain significantly improved classification performance.

Graph Classification Graph Learning +1

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

1 code implementation • 19 Dec 2021 • Rongrong Ma, Guansong Pang, Ling Chen, Anton Van Den Hengel

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs.

Anomaly Detection Knowledge Distillation

Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks

no code implementations • 4 Jan 2019 • Rongrong Ma, Jianyu Miao, Lingfeng Niu, Peng Zhang

To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously.