no code implementations • 11 Apr 2024 • Kailong Wu, Yule Xie, Jiaxin Ding, Yuxiang Ren, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.
no code implementations • 6 Mar 2024 • Kaiwei Zhang, Yange Lin, Guangcheng Wu, Yuxiang Ren, Xuecang Zhang, Bo wang, XiaoYu Zhang, Weitao Du
This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies.
no code implementations • 6 Feb 2024 • Heng Zhou, Zhetao Guo, Shuhong Liu, Lechen Zhang, Qihao Wang, Yuxiang Ren, Mingrui Li
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints.
no code implementations • 24 Nov 2023 • Shengyin Sun, Yuxiang Ren, Chen Ma, Xuecang Zhang
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).
no code implementations • 15 Oct 2023 • Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, Xuecang Zhang
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
no code implementations • 30 Dec 2021 • Jiyang Bai, Yuxiang Ren, Jiawei Zhang
We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.
1 code implementation • 5 Nov 2021 • Tianyu Zhang, Yuxiang Ren, Wenzheng Feng, Weitao Du, Xuecang Zhang
In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).
no code implementations • 27 Jan 2021 • Yuxiang Ren, Bo wang, Jiawei Zhang, Yi Chang
AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data.
1 code implementation • 14 Jan 2021 • Yuxiang Ren, Jiyang Bai, Jiawei Zhang
Graph classification is a critical research problem in many applications from different domains.
no code implementations • 17 Feb 2020 • Jiyang Bai, Yuxiang Ren, Jiawei Zhang
To deal with these problems, in this paper, we propose a general subgraph-based training framework, namely Ripple Walk Training (RWT), for deep and large graph neural networks.
no code implementations • 5 Feb 2020 • Yuxiang Ren, Jiawei Zhang
In addition, the experiment proved the expandability and generalizability of our for graph representation learning and other node classification related applications in heterogeneous graphs.
no code implementations • 23 Dec 2019 • Yuxiang Ren, Hao Zhu, Jiawei Zhang, Peng Dai, Liefeng Bo
Existing fraud detection methods try to identify unexpected dense subgraphs and treat related nodes as suspicious.
1 code implementation • 19 Nov 2019 • Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, Jiawei Zhang
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Ranked #7 on Heterogeneous Node Classification on DBLP (PACT) 14k
no code implementations • 26 Jul 2019 • Jiyang Bai, Yuxiang Ren, Jiawei Zhang
To resolve this problem and further maximize the advantages of genetic algorithm with base learners, we propose to implement the boosting strategy for input model training, which can subsequently improve the effectiveness of genetic algorithm.
no code implementations • 25 Jul 2019 • Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Optimization algorithms with momentum, e. g., (ADAM), have been widely used for building deep learning models due to the faster convergence rates compared with stochastic gradient descent (SGD).