24 citations · 37 across the 21 of their papers we have counts for
7 papers · 1 filter
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
Yujie Mo, Zhihe Lu, Runpeng Yu +2
Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clusteri…
HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
Yujie Mo, Runpeng Yu, Xiaofeng Zhu +1
The "pre-train, prompt-tuning'' paradigm has demonstrated impressive performance for tuning pre-trained heterogeneous graph neural networks (HGNNs) by mitigating the gap between pr…
Attention Prompting on Image for Large Vision-Language Models
Runpeng Yu, Weihao Yu, Xinchao Wang
Compared with Large Language Models (LLMs), Large Vision-Language Models (LVLMs) can also accept images as input, thus showcasing more interesting emergent capabilities and demonst…
KAN or MLP: A Fairer Comparison
Runpeng Yu, Weihao Yu, Xinchao Wang
This paper does not introduce a novel method. Instead, it offers a fairer and more comprehensive comparison of KAN and MLP models across various tasks, including machine learning,…
Encapsulating Knowledge in One Prompt
Qi Li, Runpeng Yu, Xinchao Wang
This paradigm encapsulates knowledge from various models into a solitary prompt without altering the original models or requiring access to the training data, which enables us to a…
Neural Lineage
Runpeng Yu, Xinchao Wang
Given a well-behaved neural network, is possible to identify its parent, based on which it was tuned? In this paper, we introduce a novel task known as neural lineage detection, ai…
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Yutong Xia, Runpeng Yu, Yuxuan Liang +3
Graph Neural Networks have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of a…