449 citations · 768 across the 44 of their papers we have counts for
13 papers · 1 filter
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
Xinyi Gao, Wentao Zhang, Junliang Yu +4
Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge…
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation
Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen +1
As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers ha…
Graph Condensation for Inductive Node Representation Learning
Xinyi Gao, Tong Chen, Yilong Zang +4
Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. T…
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Xin Zheng, Miao Zhang, Chunyang Chen +3
Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learni…
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT
Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen +1
Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), a…
Semi-decentralized Federated Ego Graph Learning for Recommendation
Liang Qu, Ningzhi Tang, Ruiqi Zheng +4
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as…
Efficient Graph Neural Network Inference at Large Scale
Xinyi Gao, Wentao Zhang, Yingxia Shao +3
Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. However, the enormous size of large-scale graphs hinders their applications un…
A Survey of Machine Unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren +4
Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (M…
Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
Yu Yang, Hongzhi Yin, Jiannong Cao +4
Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existin…
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Guanhua Ye, Hongzhi Yin, Tong Chen +3
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learn…
On Node Features for Graph Neural Networks
Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang +2
Graph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning pr…
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen +3
In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As…
Parallel Computation of Graph Embeddings
Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang +4
Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph propert…