167 citations · 203 across the 17 of their papers we have counts for
20 papers
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
Ziyu Zheng, Zhengshun Du, Yaming Yang +5
Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs. However, existing me…
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly r…
Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment
Ziyu Zheng, Yaming Yang, Zhe Wang +2
While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHGs) remains a formidable challe…
Dynamic Network-Based Two-Stage Time Series Forecasting for Affiliate Marketing
Zhe Wang, Yaming Yang, Ziyu Guan +4
In recent years, affiliate marketing has emerged as a revenue-sharing strategy where merchants collaborate with promoters to promote their products. It not only increases product e…
Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph
Ziyu Zheng, Yaming Yang, Ziyu Guan +3
The ``pre-train, prompt" paradigm, designed to bridge the gap between pre-training tasks and downstream objectives, has been extended from the NLP domain to the graph domain and ha…
ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
Weigang Lu, Ziyu Guan, Wei Zhao +5
GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs).…
H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance
Shijun Guo, Haoran Xu, Yaming Yang +4
The openness of social media enables the free exchange of opinions, but it also presents challenges in guiding opinion evolution towards global consensus. Existing methods often di…
Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Real-world networks usually have a property of node heterophily, that is, the connected nodes usually have different features or different labels. This heterophily issue has been e…
Discrepancy-Aware Graph Mask Auto-Encoder
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Masked Graph Auto-Encoder, a powerful graph self-supervised training paradigm, has recently shown superior performance in graph representation learning. Existing works typically re…
Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree
Yaming Yang, Zhe Wang, Ziyu Guan +3
Entity Alignment (EA) is to link potential equivalent entities across different knowledge graphs (KGs). Most existing EA methods are supervised as they require the supervision of s…
AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
Weigang Lu, Ziyu Guan, Wei Zhao +4
Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio in the image domain. Recently, the concept o…
Aligning Multiple Knowledge Graphs in a Single Pass
Yaming Yang, Zhe Wang, Ziyu Guan +5
Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods m…
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation
Weigang Lu, Ziyu Guan, Wei Zhao +1
Graph Neural Networks (GNNs) have revolutionized graph-based machine learning, but their heavy computational demands pose challenges for latency-sensitive edge devices in practical…
Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels
Guochen Ma, Xiaopeng Jiao, Jianjun Mu +2
Marker code is an effective coding scheme to protect data from insertions and deletions. It has potential applications in future storage systems, such as DNA storage and racetrack…
NodeMixup: Tackling Under-Reaching for Graph Neural Networks
Weigang Lu, Ziyu Guan, Wei Zhao +2
Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled…
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning
Weigang Lu, Ziyu Guan, Wei Zhao +5
Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions…
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
Yaming Yang, Ziyu Guan, Zhe Wang +4
Recent self-supervised pre-training methods on Heterogeneous Information Networks (HINs) have shown promising competitiveness over traditional semi-supervised Heterogeneous Graph N…
Entropy Induced Pruning Framework for Convolutional Neural Networks
Yiheng Lu, Ziyu Guan, Yaming Yang +3
Structured pruning techniques have achieved great compression performance on convolutional neural networks for image classification task. However, the majority of existing methods…
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
Weigang Lu, Yibing Zhan, Binbin Lin +6
Graph Convolutional Networks (GCNs) suffer from performance degradation when models go deeper. However, earlier works only attributed the performance degeneration to over-smoothing…
Interpretable and Efficient Heterogeneous Graph Convolutional Network
Yaming Yang, Ziyu Guan, Jianxin Li +3
Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective task-specific representations of nodes in graphs. However, regarding Heterogeneous Inform…