3 citations · 4 across the 9 of their papers we have counts for
10 papers
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang, Jitao Zhao, Dongxiao He +5
Graph Prompt Learning (GPL) has emerged as a promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment bet…
A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
Di Jin, Jun Yang, Xiaobao Wang +3
As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and ins…
Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
Xiaobao Wang, Ruoxiao Sun, Yujun Zhang +4
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to back…
Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
Dongxiao He, Yongqi Huang, Jitao Zhao +2
Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations…
Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems
Runze Li, Di Jin, Xiaobao Wang +3
Graph recommendation systems have been widely studied due to their ability to effectively capture the complex interactions between users and items. However, these systems also exhi…
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
Di Jin, Jingyi Cao, Xiaobao Wang +4
Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods…
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content
Sheng Wu, Xiaobao Wang, Longbiao Wang +2
Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, dis…
Progressive Residual Extraction based Pre-training for Speech Representation Learning
Tianrui Wang, Jin Li, Ziyang Ma +8
Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the per…
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations
Sheng Wu, Jiaxing Liu, Longbiao Wang +3
Emotion Recognition in Conversations (ERC) is a popular task in natural language processing, which aims to recognize the emotional state of the speaker in conversations. While curr…
speech and noise dual-stream spectrogram refine network with speech distortion loss for robust speech recognition
Haoyu Lu, Nan Li, Tongtong Song +4
In recent years, the joint training of speech enhancement front-end and automatic speech recognition (ASR) back-end has been widely used to improve the robustness of ASR systems. T…