30 citations · 58 across the 12 of their papers we have counts for
13 papers
POSIM: A Multi-Agent Simulation Framework for Social Media Public Opinion Evolution and Governance
Yongmao Zhang, Kai Qiao, Zhengyan Wang +5
Modeling social media public opinion evolution is essential for governance decision-making. Traditional epidemic models and rule-based agent-based models (ABMs) fail to capture the…
Efficient Pretraining Model based on Multi-Scale Local Visual Field Feature Reconstruction for PCB CT Image Element Segmentation
Chen Chen, Kai Qiao, Jie Yang +2
Element segmentation is a key step in nondestructive testing of Printed Circuit Boards (PCB) based on Computed Tomography (CT) technology. In recent years, the rapid development of…
Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective
Shuhao Shi, Kai Qiao, Zhengyan Wang +4
The presence of a large number of bots on social media has adverse effects. The graph neural network (GNN) can effectively leverage the social relationships between users and achie…
RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection
Shuhao Shi, Kai Qiao, Jie Yang +3
The presence of a large number of bots on social media leads to adverse effects. Although Random forest algorithm is widely used in bot detection and can significantly enhance the…
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection
Shuhao Shi, Kai Qiao, Jie Yang +3
The presence of a large number of bots in Online Social Networks (OSN) leads to undesirable social effects. Graph neural networks (GNNs) are effective in detecting bots as they uti…
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
Shuhao Shi, Kai Qiao, Jian Chen +5
The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation qual…
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
Shuhao Shi, Jian Chen, Kai Qiao +3
The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previo…
Adaptive Multi-layer Contrastive Graph Neural Networks
Shuhao Shi, Pengfei Xie, Xu Luo +4
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of…
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
S. Shi, Kai Qiao, Shuai Yang +3
The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dat…
Defense-guided Transferable Adversarial Attacks
Zifei Zhang, Kai Qiao, Jian Chen +1
Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial examples, which mislead classifiers by applying human-imperceptible perturbat…
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Kai Qiao, Chi Zhang, Jian Chen +3
On basis of functional magnetic resonance imaging (fMRI), researchers are devoted to designing visual encoding models to predict the neuron activity of human in response to present…
BigGAN-based Bayesian reconstruction of natural images from human brain activity
Kai Qiao, Jian Chen, Linyuan Wang +3
In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is di…
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Kai Qiao, Chi Zhang, Jian Chen +3
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Visual encod…