659 citations · 851 across the 26 of their papers we have counts for
11 papers · 1 filter
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
Yihao Li, Yanyi Lai, Tianchi Liao +2
With the development of practical deep learning models like generative AI, their excellent performance has brought huge economic value. For instance, ChatGPT has attracted more tha…
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Yihao Li, Yanyi Lai, Chuan Chen +1
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been d…
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
Yuecheng Li, Lele Fu, Sheng Huang +3
Recently, graph contrastive learning (GCL) has emerged as one of the optimal solutions for node-level and supervised tasks. However, for structure-related and unsupervised tasks su…
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Yuecheng Li, Jialong Chen, Chuan Chen +2
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods ha…
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
Jining Wang, Chuan Chen, Zibin Zheng +1
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. A…
Contextual Dictionary Lookup for Knowledge Graph Completion
Jining Wang, Delai Qiu, YouMing Liu +4
Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KG…
Migrate Demographic Group For Fair GNNs
YanMing Hu, TianChi Liao, JiaLong Chen +3
Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a co…
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Chuan Chen, Zhenpeng Wu, Yanyi Lai +3
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development. The content generated by related applications, such as text, images and audio,…
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Lin Shu, Chuan Chen, Zibin Zheng
Graph contrastive learning defines a contrastive task to pull similar instances close and push dissimilar instances away. It learns discriminative node embeddings without supervise…
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
YanMing Hu, Chuan Chen, BoWen Deng +4
Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus…
Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges
Chuan Chen, Yuecheng Li, Zhenpeng Wu +5
Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sus…