1 citations · 1 across the 3 of their papers we have counts for
4 papers · 1 filter
Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Zeli Wang, Qingxuan Yang, Shuyin Xia +3
Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning te…
From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
Li Ni, Shuaikang Zeng, Lin Mu +1
Contrastive learning has demonstrated strong performance in attributed hypergraph clustering. Typically, existing methods based on contrastive learning first learn node embeddings…
CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks
Tao Liu, Longlong Lin, Yunfeng Yu +4
Graph Neural Networks (GNNs) have garnered substantial attention due to their remarkable capability in learning graph representations. However, real-world graphs often exhibit subs…
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Longlong Lin, Yunfeng Yu, Zihao Wang +4
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous d…