Search Results for author: Lujia Pan

Found 23 papers, 12 papers with code

Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems

no code implementations25 Oct 2024 Jindong Tian, Yuxuan Liang, Ronghui Xu, Peng Chen, Chenjuan Guo, Aoying Zhou, Lujia Pan, Zhongwen Rao, Bin Yang

To address these challenges, we propose Air-DualODE, a novel physics-informed approach that integrates dual branches of Neural ODEs for air quality prediction.

SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning

no code implementations17 Jun 2024 Kaidi Li, Tianmeng Yang, Min Zhou, Jiahao Meng, Shendi Wang, Yihui Wu, Boshuai Tan, Hu Song, Lujia Pan, Fan Yu, Zhenli Sheng, Yunhai Tong

While considerable efforts have been devoted to designing adequate fraud detectors, the interpretability of their results has often been overlooked.

Fraud Detection Triplet

Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders

no code implementations24 May 2024 Qichao Shentu, Beibu Li, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo

The significant divergence of time series data across different domains presents two primary challenges in building such a general model: (1) meeting the diverse requirements of appropriate information bottlenecks tailored to different datasets in one unified model, and (2) enabling distinguishment between multiple normal and abnormal patterns, both are crucial for effective anomaly detection in various target scenarios.

Anomaly Detection Time Series +1

ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning

no code implementations24 May 2024 Yihang Wang, Yuying Qiu, Peng Chen, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo

Enabling general time series forecasting faces two challenges: how to obtain unified representations from multi-domian time series data, and how to capture domain-specific features from time series data across various domains for adaptive transfer in downstream tasks.

Time Series Time Series Forecasting

Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling

no code implementations1 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.

Multivariate Time Series Forecasting Time Series

MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation

1 code implementation4 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.

Graph Similarity

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

1 code implementation17 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.

Active Learning Diversity +1

A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning

1 code implementation30 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.

Anomaly Detection Data Augmentation +2

Inducing Neural Collapse in Deep Long-tailed Learning

1 code implementation24 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.

MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing

1 code implementation9 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.

Multivariate Time Series Forecasting Time Series

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

1 code implementation21 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

kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning

1 code implementation4 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.

Link Prediction Node Classification +2

Hyperbolic Graph Representation Learning: A Tutorial

no code implementations8 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.

Graph Learning Graph Representation Learning +2

Discovering Representative Attribute-stars via Minimum Description Length

no code implementations27 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.

Attribute Decision Making

TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction

1 code implementation16 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.

Link Prediction

Hyperbolic Graph Neural Networks: A Review of Methods and Applications

1 code implementation28 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.

Anatomy Graph Learning

An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels

no code implementations6 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.

Pseudo Label text-classification +1

Mask-GVAE: Blind Denoising Graphs via Partition

1 code implementation8 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.

Denoising

Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks

no code implementations17 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.

Predicting Path Failure In Time-Evolving Graphs

2 code implementations10 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.

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