Search Results for author:
Found 7 papers, 3 papers with code
Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective
Recently, more and more GNN-based methods have been proposed for bot detection.
RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection
This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, called RF-GNN, which employs graph neural networks (GNNs) as the base classifiers to construct a random forest, effectively combining the advantages of ensemble learning and GNNs to improve the accuracy and robustness of the model.
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection
The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.
Ranked #1 on
Stance Detection
on MGTAB
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
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.
Adaptive Multi-layer Contrastive Graph Neural Networks
AMC-GNN generates two graph views by data augmentation and compares different layers' output embeddings of Graph Neural Network encoders to obtain feature representations, which could be used for downstream tasks.
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
In this paper, we propose a new gradient iteration framework, which redefines the relationship between the above three.
