no code implementations • 1 Mar 2024 • shiyi qi, Liangjian Wen, Yiduo Li, Yuanhang Yang, Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu
To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information.
no code implementations • 4 Nov 2023 • Junfeng Liu, Min Zhou, Shuai Ma, Lujia Pan
Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks.
no code implementations • 17 Aug 2023 • Tianmeng Yang, Min Zhou, Yujing Wang, Zhengjie Lin, Lujia Pan, Bin Cui, Yunhai Tong
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges.
1 code implementation • 30 May 2023 • Zhiyu Liang, Jianfeng Zhang, Chen Liang, Hongzhi Wang, Zheng Liang, Lujia Pan
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels.
1 code implementation • 24 Feb 2023 • Xuantong Liu, Jianfeng Zhang, Tianyang Hu, He Cao, Lujia Pan, Yuan YAO
One of the reasons is that the learned representations (i. e. features) from the imbalanced datasets are less effective than those from balanced datasets.
no code implementations • 14 Feb 2023 • Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu
Learning with imbalanced data is a challenging problem in deep learning.
1 code implementation • 9 Feb 2023 • Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu
Specifically, we find that (1) attention is not necessary for capturing temporal dependencies, (2) the entanglement and redundancy in the capture of temporal and channel interaction affect the forecasting performance, and (3) it is important to model the mapping between the input and the prediction sequence.
1 code implementation • 21 Jan 2023 • Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, Zenglin Xu
Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.
Contrastive Learning Multivariate Time Series Forecasting +2
no code implementations • 4 Dec 2022 • Menglin Yang, Min Zhou, Lujia Pan, Irwin King
The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation systems, ecosystems, financial networks, social networks, etc.
no code implementations • 8 Nov 2022 • Min Zhou, Menglin Yang, Lujia Pan, Irwin King
We first give a brief introduction to graph representation learning as well as some preliminary Riemannian and hyperbolic geometry.
no code implementations • 27 Apr 2022 • Jiahong Liu, Min Zhou, Philippe Fournier-Viger, Menglin Yang, Lujia Pan, Mourad Nouioua
However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains.
1 code implementation • 16 Apr 2022 • Min Zhou, Bisheng Li, Menglin Yang, Lujia Pan
Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications.
1 code implementation • 28 Feb 2022 • Menglin Yang, Min Zhou, Zhihao LI, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
2 code implementations • 30 Nov 2021 • Keli Zhang, Shengyu Zhu, Marcus Kalander, Ignavier Ng, Junjian Ye, Zhitang Chen, Lujia Pan
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
no code implementations • 9 Nov 2021 • Chaozheng Wang, Shuzheng Gao, Cuiyun Gao, Pengyun Wang, Wenjie Pei, Lujia Pan, Zenglin Xu
Real-world data usually present long-tailed distributions.
no code implementations • 6 Jul 2021 • Yong Wen, Marcus Kalander, Chanfei Su, Lujia Pan
E-NKCVS is empirically shown to be highly tolerant to considerable proportions of label noise and has a consistent improvement over state-of-the-art methods.
1 code implementation • 8 Feb 2021 • Jia Li, Mengzhou Liu, Honglei Zhang, Pengyun Wang, Yong Wen, Lujia Pan, Hong Cheng
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.
no code implementations • 17 Sep 2020 • Marcus Kalander, Min Zhou, Chengzhi Zhang, Hanling Yi, Lujia Pan
We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks.
2 code implementations • 10 May 2019 • Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan
Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.