Search Results for author: Dongsheng Luo

Found 27 papers, 11 papers with code

Parameterized Explainer for Graph Neural Network

3 code implementations NeurIPS 2020 Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang

The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.

Graph Classification

Explaining Time Series via Contrastive and Locally Sparse Perturbations

1 code implementation16 Jan 2024 Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen

Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.

Contrastive Learning counterfactual +1

Random Walk on Multiple Networks

1 code implementation4 Jul 2023 Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiong Yu, Jun Huan, Xiao Liu, Xiang Zhang

To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.

Link Prediction Local Community Detection +1

Time Series Contrastive Learning with Information-Aware Augmentations

1 code implementation21 Mar 2023 Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Yuncong Chen, Haifeng Chen, Xiang Zhang

A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.

Contrastive Learning Open-Ended Question Answering +2

Unsupervised Document Embedding via Contrastive Augmentation

1 code implementation26 Mar 2021 Dongsheng Luo, Wei Cheng, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Bo Zong, Yanchi Liu, Zhengzhang Chen, Dongjin Song, Haifeng Chen, Xiang Zhang

We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.

Contrastive Learning Data Augmentation +4

Parametric Augmentation for Time Series Contrastive Learning

1 code implementation16 Feb 2024 Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, Dongsheng Luo

In this study, we address this gap by analyzing time series data augmentation using information theory and summarizing the most commonly adopted augmentations in a unified format.

Contrastive Learning Data Augmentation +2

Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks

1 code implementation3 Oct 2023 Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo

An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.

Decision Making

Personalized Federated Learning via Heterogeneous Modular Networks

1 code implementation26 Oct 2022 Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.

Personalized Federated Learning

MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation

1 code implementation15 Jul 2023 Jiaxing Zhang, Dongsheng Luo, Hua Wei

Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue.

Data Augmentation

Attentive Social Recommendation: Towards User And Item Diversities

1 code implementation9 Nov 2020 Dongsheng Luo, Yuchen Bian, Xiang Zhang, Jun Huan

Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings.

Information-Aware Time Series Meta-Contrastive Learning

no code implementations29 Sep 2021 Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Haifeng Chen, Xiang Zhang

How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.

Contrastive Learning Meta-Learning +4

InfoGCL: Information-Aware Graph Contrastive Learning

no code implementations NeurIPS 2021 Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang

The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized.

Contrastive Learning Graph Classification +3

Towards Faithful and Consistent Explanations for Graph Neural Networks

no code implementations27 May 2022 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input.

Inductive Bias Network Interpretation

TopoImb: Toward Topology-level Imbalance in Learning from Graphs

no code implementations16 Dec 2022 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

To address this problem, we propose a new framework {\method} and design (1 a topology extractor, which automatically identifies the topology group for each instance with explicit memory cells, (2 a training modulator, which modulates the learning process of the target GNN model to prevent the case of topology-group-wise under-representation.

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

no code implementations7 Jan 2023 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions.

Inductive Bias

Self-Explainable Graph Neural Networks for Link Prediction

no code implementations21 May 2023 Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang, Junjie Xu, Hui Liu, Suhang Wang

Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.

Link Prediction Node Classification

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task

no code implementations15 Jul 2023 Jiaxing Zhang, Zhuomin Chen, Hao Mei, Dongsheng Luo, Hua Wei

Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.

Contrastive Learning Graph Learning +2

Learning Graph Filters for Spectral GNNs via Newton Interpolation

no code implementations16 Oct 2023 Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang

Spectral Graph Neural Networks (GNNs) are gaining attention because they can surpass the limitations of message-passing GNNs by learning spectral filters that capture essential frequency information in graph data through task supervision.

DyExplainer: Explainable Dynamic Graph Neural Networks

no code implementations25 Oct 2023 Tianchun Wang, Dongsheng Luo, Wei Cheng, Haifeng Chen, Xiang Zhang

Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships.

Contrastive Learning Link Prediction

Factorized Explainer for Graph Neural Networks

no code implementations9 Dec 2023 Rundong Huang, Farhad Shirani, Dongsheng Luo

Instead, we argue that a modified GIB principle may be used to avoid the aforementioned trivial solutions.

Rank Supervised Contrastive Learning for Time Series Classification

no code implementations31 Jan 2024 Qianying Ren, Dongsheng Luo, Dongjin Song

Recently, various contrastive learning techniques have been developed to categorize time series data and exhibit promising performance.

Classification Contrastive Learning +2

Interpreting Graph Neural Networks with In-Distributed Proxies

no code implementations3 Feb 2024 Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo

A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.

Decision Making

PAC Learnability under Explanation-Preserving Graph Perturbations

no code implementations7 Feb 2024 Xu Zheng, Farhad Shirani, Tianchun Wang, Shouwei Gao, Wenqian Dong, Wei Cheng, Dongsheng Luo

It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning.

Data Augmentation

Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data

no code implementations2 Mar 2024 Jun-En Ding, Phan Nguyen Minh Thao, Wen-Chih Peng, Jian-Zhe Wang, Chun-Cheng Chug, Min-Chen Hsieh, Yun-Chien Tseng, Ling Chen, Dongsheng Luo, Chi-Te Wang, Pei-fu Chen, Feng Liu, Fang-Ming Hung

In our experiments, we observe that clinicalBERT and PubMed-BERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction.

Diabetes Prediction

Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape

no code implementations9 Mar 2024 Tiejin Chen, Wenwang Huang, Linsey Pang, Dongsheng Luo, Hua Wei

This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated.

Classification Image Classification

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