30 citations · 44 across the 16 of their papers we have counts for
17 papers
HyperCD: Effective Source Detection in Hypergraphs via Causal Disentanglement
Yan Kang, Peican Zhu, Keke Tang +4
Source detection in hypergraphs plays an important role in mitigating the spread of harmful rumors and ensuring social stability. Current methods frequently overlook the causal ent…
Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective
Keke Tang, Xianheng Liu, Weilong Peng +5
Transferable adversarial attacks on point clouds remain challenging, as existing methods often rely on model-specific gradients or heuristics that limit generalization to unseen ar…
Optimal Transport-Induced Samples against Out-of-Distribution Overconfidence
Keke Tang, Ziyong Du, Xiaofei Wang +3
Deep neural networks (DNNs) often produce overconfident predictions on out-of-distribution (OOD) inputs, undermining their reliability in open-world environments. Singularities in…
Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges
Meixia He, Peican Zhu, Le Cheng +3
Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanis…
Transferable and Undefendable Point Cloud Attacks via Medial Axis Transform
Keke Tang, Yuze Gao, Weilong Peng +3
Studying adversarial attacks on point clouds is essential for evaluating and improving the robustness of 3D deep learning models. However, most existing attack methods are develope…
SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng +4
Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. Howe…
KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng +2
In recent years, the rampant spread of misinformation on social media has made accurate detection of multimodal fake news a critical research focus. However, previous research has…
Cage-Based Deformation for Transferable and Undefendable Point Cloud Attack
Keke Tang, Ziyong Du, Weilong Peng +4
Adversarial attacks on point clouds often impose strict geometric constraints to preserve plausibility; however, such constraints inherently limit transferability and undefendabili…
SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs
Le Cheng, Peican Zhu, Yangming Guo +3
Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics…
HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion
Le Cheng, Peican Zhu, Yangming Guo +3
Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Exist…
EOOD: Entropy-based Out-of-distribution Detection
Guide Yang, Chao Hou, Weilong Peng +4
Deep neural networks (DNNs) often exhibit overconfidence when encountering out-of-distribution (OOD) samples, posing significant challenges for deployment. Since DNNs are trained o…
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field
Keke Tang, Weiyao Ke, Weilong Peng +5
Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Traditional solutions strictly limit point displa…
Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges
Meixia He, Peican Zhu, Keke Tang +1
Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by grad…
Node Injection Attack Based on Label Propagation Against Graph Neural Network
Peican Zhu, Zechen Pan, Keke Tang +3
Graph Neural Network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction and graph classification. The key to the su…
A General Black-box Adversarial Attack on Graph-based Fake News Detectors
Peican Zhu, Zechen Pan, Yang Liu +3
Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the constructi…
GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
Le Cheng, Peican Zhu, Keke Tang +2
Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neu…
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection
Peican Zhu, Xin Hou, Keke Tang +2
Along with the flourish of the information age, massive amounts of data are generated day by day. Due to the large-scale and high-dimensional characteristics of these data, it is o…