Search Results for author: Xiao Luo

Found 49 papers, 11 papers with code

Fast Inference of Removal-Based Node Influence

1 code implementation13 Mar 2024 Weikai Li, Zhiping Xiao, Xiao Luo, Yizhou Sun

We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node.

Adversarial Attack counterfactual +1

A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

no code implementations7 Mar 2024 Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang

To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.

Fraud Detection

COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting

no code implementations2 Mar 2024 Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, Ming Zhang

Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships.

COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing

no code implementations26 Feb 2024 Yihang Zhou, Qingqing Long, Yuchen Yan, Xiao Luo, Zeyu Dong, Xuezhi Wang, Zhen Meng, Pengfei Wang, Yuanchun Zhou

Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.

Attribute Contrastive Learning +1

An Evaluation of Large Language Models in Bioinformatics Research

no code implementations21 Feb 2024 Hengchuang Yin, Zhonghui Gu, Fanhao Wang, Yiparemu Abuduhaibaier, Yanqiao Zhu, Xinming Tu, Xian-Sheng Hua, Xiao Luo, Yizhou Sun

Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities.

GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling

no code implementations29 Jan 2024 Wei Ju, Yiyang Gu, Zhengyang Mao, Ziyue Qiao, Yifang Qin, Xiao Luo, Hui Xiong, Ming Zhang

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.

Adversarial Robustness Contrastive Learning +3

PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering

no code implementations23 Jan 2024 Yifang Qin, Wei Ju, Xiao Luo, Yiyang Gu, Zhiping Xiao, Ming Zhang

Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations.

Collaborative Filtering Recommendation Systems

A Survey on Graph Neural Networks in Intelligent Transportation Systems

no code implementations1 Jan 2024 Hourun Li, Yusheng Zhao, Zhengyang Mao, Yifang Qin, Zhiping Xiao, Jiaqi Feng, Yiyang Gu, Wei Ju, Xiao Luo, Ming Zhang

However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention.

Autonomous Vehicles

TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems

no code implementations10 Oct 2023 Zijie Huang, Wanjia Zhao, Jingdong Gao, Ziniu Hu, Xiao Luo, Yadi Cao, Yuanzhou Chen, Yizhou Sun, Wei Wang

Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling.

Inductive Bias Physical Simulations

Large Language Models Can Be Good Privacy Protection Learners

no code implementations3 Oct 2023 Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng

To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.

ALEX: Towards Effective Graph Transfer Learning with Noisy Labels

no code implementations26 Sep 2023 Jingyang Yuan, Xiao Luo, Yifang Qin, Zhengyang Mao, Wei Ju, Ming Zhang

Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios.

Contrastive Learning Graph Learning +2

Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting

no code implementations21 Sep 2023 Yusheng Zhao, Xiao Luo, Wei Ju, Chong Chen, Xian-Sheng Hua, Ming Zhang

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past.

Semi-supervised Domain Adaptation in Graph Transfer Learning

no code implementations19 Sep 2023 Ziyue Qiao, Xiao Luo, Meng Xiao, Hao Dong, Yuanchun Zhou, Hui Xiong

To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes.

Semi-supervised Domain Adaptation Transfer Learning +1

Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model

no code implementations11 Sep 2023 Neel Bhate, Ansh Mittal, Zhe He, Xiao Luo

We utilize de-identified real-world clinical notes annotated for demographics, various social determinants, and family history information.

NER Semantic Similarity +2

Redundancy-Free Self-Supervised Relational Learning for Graph Clustering

1 code implementation9 Sep 2023 Si-Yu Yi, Wei Ju, Yifang Qin, Xiao Luo, Luchen Liu, Yong-Dao Zhou, Ming Zhang

Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years.

Attribute Clustering +4

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

no code implementations31 Aug 2023 Siyu Yi, Zhengyang Mao, Wei Ju, Yongdao Zhou, Luchen Liu, Xiao Luo, Ming Zhang

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution.

Contrastive Learning Graph Classification +2

RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

no code implementations4 Aug 2023 Zhengyang Mao, Wei Ju, Yifang Qin, Xiao Luo, Ming Zhang

Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes.

Graph Classification Retrieval

TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer

2 code implementations27 Jul 2023 Zhen Qin, Dong Li, Weigao Sun, Weixuan Sun, Xuyang Shen, Xiaodong Han, Yunshen Wei, Baohong Lv, Xiao Luo, Yu Qiao, Yiran Zhong

TransNormerLLM evolves from the previous linear attention architecture TransNormer by making advanced modifications that include positional embedding, linear attention acceleration, gating mechanisms, tensor normalization, and inference acceleration and stabilization.

Language Modelling Large Language Model

CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

no code implementations20 Jun 2023 Song Jiang, Zijie Huang, Xiao Luo, Yizhou Sun

We model a multi-agent dynamical system as a graph and propose CounterFactual GraphODE (CF-GODE), a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units.

Causal Inference counterfactual

Learning on Graphs under Label Noise

no code implementations14 Jun 2023 Jingyang Yuan, Xiao Luo, Yifang Qin, Yusheng Zhao, Wei Ju, Ming Zhang

Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise.

Anomaly Detection Contrastive Learning +2

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

no code implementations8 Jun 2023 Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo

Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.

Contrastive Learning Domain Adaptation +2

Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains

no code implementations2 Jun 2023 Jinjin Cai, Sudip Vhaduri, Xiao Luo

Rapid discovery of new diseases, such as COVID-19 can enable a timely epidemic response, preventing the large-scale spread and protecting public health.

Contrastive Learning

Towards Semi-supervised Universal Graph Classification

no code implementations31 May 2023 Xiao Luo, Yusheng Zhao, Yifang Qin, Wei Ju, Ming Zhang

To tackle class shifts, we estimate the certainty of unlabeled graphs using multiple subgraphs, which facilities the discovery of unlabeled data from unknown categories.

Graph Classification

PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction

1 code implementation19 May 2023 Hao Wu, Wei Xiong, Fan Xu, Xiao Luo, Chong Chen, Xian-Sheng Hua, Haixin Wang

In this paper, we investigate the challenge of spatio-temporal video prediction, which involves generating future videos based on historical data streams.

Video Prediction

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

no code implementations23 Apr 2023 Wei Ju, Xiao Luo, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng, Xian-Sheng Hua, Ming Zhang

The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data.

Graph Classification

A Diffusion model for POI recommendation

1 code implementation14 Apr 2023 Yifang Qin, Hongjun Wu, Wei Ju, Xiao Luo, Ming Zhang

In this paper, we propose Diff-POI: a Diffusion-based model that samples the user's spatial preference for the next POI recommendation.

Learning Graph ODE for Continuous-Time Sequential Recommendation

no code implementations14 Apr 2023 Yifang Qin, Wei Ju, Hongjun Wu, Xiao Luo, Ming Zhang

Technically, GDERec is characterized by an autoregressive graph ordinary differential equation consisting of two components, which are parameterized by two tailored graph neural networks (GNNs) respectively to capture user preference from the perspective of hybrid dynamical systems.

Sequential Recommendation

A Comprehensive Survey on Deep Graph Representation Learning

no code implementations11 Apr 2023 Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, Junwei Yang, Jingyang Yuan, Yusheng Zhao, Yifan Wang, Xiao Luo, Ming Zhang

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.

Graph Embedding Graph Representation Learning

LION: Implicit Vision Prompt Tuning

no code implementations17 Mar 2023 Haixin Wang, Jianlong Chang, Xiao Luo, Jinan Sun, Zhouchen Lin, Qi Tian

Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs.

Transfer Learning

Prototypical Mixing and Retrieval-Based Refinement for Label Noise-Resistant Image Retrieval

no code implementations ICCV 2023 Xinlong Yang, Haixin Wang, Jinan Sun, Shikun Zhang, Chong Chen, Xian-Sheng Hua, Xiao Luo

This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization.

Image Retrieval Memorization +1

GLCC: A General Framework for Graph-Level Clustering

no code implementations21 Oct 2022 Wei Ju, Yiyang Gu, Binqi Chen, Gongbo Sun, Yifang Qin, Xingyuming Liu, Xiao Luo, Ming Zhang

In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs.

Clustering Contrastive Learning +2

Kernel-based Substructure Exploration for Next POI Recommendation

1 code implementation8 Oct 2022 Wei Ju, Yifang Qin, Ziyue Qiao, Xiao Luo, Yifan Wang, Yanjie Fu, Ming Zhang

To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way.

Recommendation Systems

Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning

4 code implementations3 Oct 2022 Weicong Liang, Yuhui Yuan, Henghui Ding, Xiao Luo, WeiHong Lin, Ding Jia, Zheng Zhang, Chao Zhang, Han Hu

Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.

Clustering Depth Estimation +6

Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns

no code implementations13 Mar 2022 Md. Ahsanul Kabir, AlJohara Almulhim, Xiao Luo, Mohammad Al Hasan

Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences.

Information Retrieval Retrieval +1

DNA-GCN: Graph convolutional networks for predicting DNA-protein binding

1 code implementation2 Jun 2021 Yuhang Guo, Xiao Luo, Liang Chen, Minghua Deng

Predicting DNA-protein binding is an important and classic problem in bioinformatics.

Specificity

Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation

1 code implementation25 May 2021 Xiao Luo, Daqing Wu, Yiyang Gu, Chong Chen, Luchen Liu, Jinwen Ma, Ming Zhang, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua

Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior.

Collaborative Filtering Metric Learning +1

Deep Unsupervised Hashing by Distilled Smooth Guidance

no code implementations13 May 2021 Xiao Luo, Zeyu Ma, Daqing Wu, Huasong Zhong, Chong Chen, Jinwen Ma, Minghua Deng

Hashing has been widely used in approximate nearest neighbor search for its storage and computational efficiency.

Clustering Computational Efficiency +1

CIMON: Towards High-quality Hash Codes

no code implementations15 Oct 2020 Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen, Minghua Deng, Jinwen Ma, Zhongming Jin, Jianqiang Huang, Xian-Sheng Hua

However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning.

Computational Efficiency Image Augmentation +4

A Survey on Deep Hashing Methods

no code implementations4 Mar 2020 Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua

Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining.

Deep Hashing Domain Adaptation +4

Biomedical Document Clustering and Visualization based on the Concepts of Diseases

no code implementations22 Oct 2018 Setu Shah, Xiao Luo

In this research, a vector representation of concepts of diseases and similarity measurement between concepts are proposed.

Clustering

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