1.2k citations · 4.2k across the 61 of their papers we have counts for
62 papers
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Jie Liao, Jintang Li, Liang Chen +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the…
Oversmoothing: A Nightmare for Graph Contrastive Learning?
Jintang Li, Wangbin Sun, Ruofan Wu +3
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contra…
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
Qichao Wang, Huan Ma, Wentao Wei +8
The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed thr…
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
Jintang Li, Sheng Tian, Ruofan Wu +6
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solution…
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Jintang Li, Huizhe Zhang, Ruofan Wu +5
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensi…
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services
Hui Dou, Shanshan Zhu, Yiwen Zhang +2
Hyper-parameters optimization (HPO) is vital for machine learning models. Besides model accuracy, other tuning intentions such as model training time and energy consumption are als…
Spectral Adversarial Training for Robust Graph Neural Network
Jintang Li, Jiaying Peng, Liang Chen +3
Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as adversarial examples. To address this issue…
Listening to Users' Voice: Automatic Summarization of Helpful App Reviews
Cuiyun Gao, Yaoxian Li, Shuhan Qi +4
App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features…
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
Taolin Zhang, Chuan Chen, Yaomin Chang +2
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some prac…
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Jintang Li, Zhouxin Yu, Zulun Zhu +6
Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
A Survey of Deep Learning Models for Structural Code Understanding
Ruoting Wu, Yuxin Zhang, Qibiao Peng +2
In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approach…
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Yuansheng Wang, Wangbin Sun, Kun Xu +3
Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the major…
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
Jintang Li, Ruofan Wu, Wangbin Sun +6
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding. However, there is a lack of theoretical understand…
GUARD: Graph Universal Adversarial Defense
Jintang Li, Jie Liao, Ruofan Wu +5
Graph convolutional networks (GCNs) have been shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in secu…
Complex Network Analysis of the Bitcoin Transaction Network
Bishenghui Tao, Hong-Ning Dai, Jiajing Wu +3
In this brief, we conduct a complex-network analysis of the Bitcoin transaction network. In particular, we design a new sampling method, namely random walk with flying-back (RWFB),…
MMES: Mixture Model based Evolution Strategy for Large-Scale Optimization
Xiaoyu He, Zibin Zheng, Yuren Zhou
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to the Gaussian sampling in CM…
TRacer: Scalable Graph-based Transaction Tracing for Account-based Blockchain Trading Systems
Zhiying Wu, Jieli Liu, Jiajing Wu +1
Security incidents such as scams and hacks, have become a major threat to the health of the blockchain ecosystem, causing billions of dollars in losses each year for blockchain use…
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Jintang Li, Bingzhe Wu, Chengbin Hou +5
Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despit…
Neighboring Backdoor Attacks on Graph Convolutional Network
Liang Chen, Qibiao Peng, Jintang Li +4
Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., t…
Understanding the Decentralization of DPoS: Perspectives From Data-Driven Analysis on EOSIO
Jieli Liu, Weilin Zheng, Dingyuan Lu +2
Recently, many Delegated Proof-of-Stake (DPoS)-based blockchains have been widely used in decentralized applications, such as EOSIO, Tron, and Binance Smart Chain. Compared with tr…
Fusing Blockchain and AI with Metaverse: A Survey
Qinglin Yang, Yetong Zhao, Huawei Huang +3
Metaverse as the latest buzzword has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows ava…
Understanding Structural Vulnerability in Graph Convolutional Networks
Liang Chen, Jintang Li, Qibiao Peng +3
Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to adversarial attacks on the graph structure. Although multiple works have been proposed to impro…
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
Chunjiang Che, Xiaoli Li, Chuan Chen +2
Federated learning allows multiple participants to collaboratively train an efficient model without exposing data privacy. However, this distributed machine learning training metho…