33 citations · 38 across the 13 of their papers we have counts for
13 papers
Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
Bochen Lin, Jianxiang Yu, Jiayi Wu +3
Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Existing approaches generally…
Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon
Xuchen Ma, Jianxiang Yu, Wenming Shao +2
Social media platforms have experienced a significant rise in toxic content, including abusive language and discriminatory remarks, presenting growing challenges for content modera…
Relation-Aware Graph Foundation Model
Jianxiang Yu, Jiapeng Zhu, Hao Qian +4
In recent years, large language models (LLMs) have demonstrated remarkable capability to generalize across diverse natural language processing tasks, inspiring the development of g…
Can Large Language Models Act as Ensembler for Multi-GNNs?
Hanqi Duan, Yao Cheng, Jianxiang Yu +2
Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, GNNs lack the inherent semantic understanding capability of rich text…
Boosting Graph Foundation Model from Structural Perspective
Yao Cheng, Yige Zhao, Jianxiang Yu +1
Graph foundation models have recently attracted significant attention due to its strong generalizability. Although existing methods resort to language models to learn unified seman…
Resist Label Noise with PGM for Graph Neural Networks
Qingqing Ge, Jianxiang Yu, Zeyuan Zhao +1
While robust graph neural networks (GNNs) have been widely studied for graph perturbation and attack, those for label noise have received significantly less attention. Most existin…
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Yige Zhao, Jianxiang Yu, Yao Cheng +4
Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existi…
HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness
Zeyuan Zhao, Qingqing Ge, Anfeng Cheng +3
Heterogeneous graph neural networks(HGNNs) have recently shown impressive capability in modeling heterogeneous graphs that are ubiquitous in real-world applications. Most existing…
Context-aware Session-based Recommendation with Graph Neural Networks
Zhihui Zhang, JianXiang Yu, Xiang Li
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich cont…
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Qingqing Ge, Zeyuan Zhao, Yiding Liu +4
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to vario…
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
Chenghua Gong, Xiang Li, Jianxiang Yu +3
Graphs have become an important modeling tool for web applications, and Graph Neural Networks (GNNs) have achieved great success in graph representation learning. However, the perf…
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs
Jianxiang Yu, Yuxiang Ren, Chenghua Gong +3
Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for…
Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples
Jianxiang Yu, Qingqing Ge, Xiang Li +1
Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic relat…