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Found 48 papers, 31 papers with code
A Review of Graph Neural Networks in Epidemic Modeling
In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions.
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
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.
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
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.
Precedence-Constrained Winter Value for Effective Graph Data Valuation
Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation.
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
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
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.
Augment with Care: Enhancing Graph Contrastive Learning with Selective Spectrum Perturbation
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.
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
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.
Globally Interpretable Graph Learning via Distribution Matching
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns.
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
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.
Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study
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
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
It aims to predict unseen edges by learning representations for all the entities and relations in a KG.
Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation
In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
Test-Time Training for Graph Neural Networks
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.
Empowering Graph Representation Learning with Test-Time Graph Transformation
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.
Learning Representations for Hyper-Relational Knowledge Graphs
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.
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.
Condensing Graphs via One-Step Gradient Matching
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.
Identifying Critical LMS Features for Predicting At-risk Students
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
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.
Graph Data Augmentation for Graph Machine Learning: A Survey
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.
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
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
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
Graph Condensation for Graph Neural Networks
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
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.
Ranked #16 on
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on ogbg-molpcba
Towards Feature Overcorrelation in Deeper Graph Neural Networks
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.
Graph Trend Filtering Networks for Recommendations
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.
Localized Graph Collaborative Filtering
These methods often make recommendations based on the learned user and item embeddings.
Jointly Attacking Graph Neural Network and its Explanations
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
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
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
Graph Feature Gating Networks
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
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.
Improvement of Normal Estimation for PointClouds via Simplifying Surface Fitting
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
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
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.
Self-supervised Learning on Graphs: Deep Insights and New Direction
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
Customized Graph Neural Networks
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
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
As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.
Incorporating Emoji Descriptions Improves Tweet Classification
Tweets are short messages that often include specialized language such as hashtags and emojis.
Learning Feature Representations for Keyphrase Extraction
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.
