Search Results for author: Wei Jin

Found 46 papers, 30 papers with code

Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective

no code implementations13 Feb 2024 Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang

These insights have empowered us to develop a novel GNN backbone model, DGAT, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture.

Out-of-Distribution Generalization

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

1 code implementation7 Feb 2024 Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You

Specifically, we employ a curriculum learning strategy to train expert trajectories with more diverse supervision signals from the original graph, and then effectively transfer the information into the condensed graph with expanding window matching.

A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

1 code implementation29 Jan 2024 Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B. Aditya Prakash, Wei Jin

In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation.

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models

1 code implementation1 Nov 2023 ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce Ho, Carl Yang

Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.

Clinical Knowledge Knowledge Graphs +1

Augment with Care: Enhancing Graph Contrastive Learning with Selective Spectrum Perturbation

no code implementations20 Oct 2023 Kaiqi Yang, Haoyu Han, Wei Jin, Hui Liu

Existing augmentation views with perturbed graph structures are usually based on random topology corruption in the spatial domain; however, from perspectives of the spectral domain, this approach may be ineffective as it fails to pose tailored impacts on the information of different frequencies, thus weakening the agreement between the augmentation views.

Contrastive Learning

Label-free Node Classification on Graphs with Large Language Models (LLMS)

1 code implementation7 Oct 2023 Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang

In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.

Node Classification

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

2 code implementations7 Jul 2023 Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang

The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.

General Knowledge Node Classification

Globally Interpretable Graph Learning via Distribution Matching

no code implementations18 Jun 2023 Yi Nian, Yurui Chang, Wei Jin, Lu Lin

Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns.

Graph Classification Graph Learning

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

1 code implementation NeurIPS 2023 Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang

Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.

Node Classification

Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study

no code implementations30 Mar 2023 Chunzhi Yi, Baichun Wei, Wei Jin, Jianfei Zhu, Seungmin Rho, ZhiYuan Chen, Feng Jiang

Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse.

Single-Cell Multimodal Prediction via Transformers

1 code implementation1 Mar 2023 Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, Jiliang Tang

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics.

Toward Degree Bias in Embedding-Based Knowledge Graph Completion

1 code implementation10 Feb 2023 Harry Shomer, Wei Jin, Wentao Wang, Jiliang Tang

It aims to predict unseen edges by learning representations for all the entities and relations in a KG.

Data Augmentation

Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation

1 code implementation6 Feb 2023 Hongzhi Wen, Wenzhuo Tang, Wei Jin, Jiayuan Ding, Renming Liu, Xinnan Dai, Feng Shi, Lulu Shang, Hui Liu, Yuying Xie

In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.

Computational Efficiency Imputation

Deep Learning in Single-Cell Analysis

6 code implementations22 Oct 2022 Dylan Molho, Jiayuan Ding, Zhaoheng Li, Hongzhi Wen, Wenzhuo Tang, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang

Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.

Cell Segmentation Imputation

Test-Time Training for Graph Neural Networks

no code implementations17 Oct 2022 Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie

To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.

Graph Classification Self-Supervised Learning +1

Empowering Graph Representation Learning with Test-Time Graph Transformation

1 code implementation7 Oct 2022 Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah

In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance.

Drug Discovery Graph Representation Learning +2

Learning Representations for Hyper-Relational Knowledge Graphs

1 code implementation30 Aug 2022 Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, Jiliang Tang

It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers.

Condensing Graphs via One-Step Gradient Matching

3 code implementations15 Jun 2022 Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin

However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.

Dataset Condensation

Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective

1 code implementation15 Jun 2022 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.

Drug Discovery Feature Correlation

Identifying Critical LMS Features for Predicting At-risk Students

no code implementations27 Apr 2022 Ying Guo, Cengiz Gunay, Sairam Tangirala, David Kerven, Wei Jin, Jamye Curry Savage, Seungjin Lee

Unsupervised learning was also used to group students into different clusters based on the similarities in their interaction/involvement with LMS.


Graph Neural Networks for Multimodal Single-Cell Data Integration

1 code implementation3 Mar 2022 Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, Jiliang Tang

Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.

Data Integration

Graph Data Augmentation for Graph Machine Learning: A Survey

1 code implementation17 Feb 2022 Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang

Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain.

BIG-bench Machine Learning Data Augmentation

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

1 code implementation1 Jan 2022 Enyan Dai, Wei Jin, Hui Liu, Suhang Wang

To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes.

Graph Neural Networks with Adaptive Residual

1 code implementation NeurIPS 2021 Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang

Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.

Graph Representation Learning

Graph Condensation for Graph Neural Networks

2 code implementations ICLR 2022 Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah

Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.

From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

2 code implementations ICLR 2022 Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah

We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN.

Towards Feature Overcorrelation in Deeper Graph Neural Networks

no code implementations29 Sep 2021 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.

Feature Correlation Graph Representation Learning

Graph Trend Filtering Networks for Recommendations

1 code implementation12 Aug 2021 Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li

The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.

Collaborative Filtering Graph Representation Learning +1

Jointly Attacking Graph Neural Network and its Explanations

no code implementations7 Aug 2021 Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, JianPing Wang, Charu Aggarwal

Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.

Elastic Graph Neural Networks

1 code implementation5 Jul 2021 Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang

While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.

Automated Self-Supervised Learning for Graphs

1 code implementation ICLR 2022 Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang

Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.

Clustering Node Classification +2

Graph Feature Gating Networks

no code implementations10 May 2021 Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang

Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.


The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification

no code implementations Findings (ACL) 2021 Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang

The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.

Fairness General Classification +2

Improvement of Normal Estimation for PointClouds via Simplifying Surface Fitting

no code implementations21 Apr 2021 Jun Zhou, Wei Jin, Mingjie Wang, Xiuping Liu, Zhiyang Li, Zhaobin Liu

Firstly, a dynamic top-k selection strategy is introduced to better focus on the most critical points of a given patch, and the points selected by our learning method tend to fit a surface by way of a simple tangent plane, which can dramatically improve the normal estimation results of patches with sharp corners or complex patterns.

Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching

no code implementations30 Mar 2021 Jun Zhou, Wei Jin, Mingjie Wang, Xiuping Liu, Zhiyang Li, Zhaobin Liu

At the stitching stage, we use the learned weights of multi-branch planar experts and distance weights between points to select the best normal from the overlapping parts.


Node Similarity Preserving Graph Convolutional Networks

1 code implementation19 Nov 2020 Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang

Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.

Graph Representation Learning Self-Supervised Learning

Customized Graph Neural Networks

no code implementations22 May 2020 Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang

Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.

General Classification Graph Classification

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

3 code implementations13 May 2020 Ya-Xin Li, Wei Jin, Han Xu, Jiliang Tang

DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field.

Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

3 code implementations2 Mar 2020 Wei Jin, Ya-Xin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang

As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.

Adversarial Attack

Learning Feature Representations for Keyphrase Extraction

no code implementations5 Jan 2018 Corina Florescu, Wei Jin

In supervised approaches for keyphrase extraction, a candidate phrase is encoded with a set of hand-crafted features and machine learning algorithms are trained to discriminate keyphrases from non-keyphrases.

BIG-bench Machine Learning Feature Engineering +1

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