Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning
Algorithm fairness has become a trending topic, and it has a great impact on social welfare. Among different fairness definitions, path-specific causal fairness is a widely adopted one with great potentials, as it distin…
FairnessGraph structure learningPredictionDynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this rep…
Age And Gender ClassificationGender ClassificationGraph structure learningTime Series AnalysisLabel-informed Graph Structure Learning for Node Classification
Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different grap…
ClassificationGraph structure learningNode ClassificationA Survey on Graph Structure Learning: Progress and Opportunities
Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensitive to the quality of the given graph st…
Graph structure learningSurveySLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer a task-specific latent…
Graph structure learningDiscrete Graph Structure Learning for Forecasting Multiple Time Series
Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in en…
Graph structure learningTime SeriesTime Series AnalysisTime Series ForecastingDynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of wate…
Graph Neural NetworkGraph structure learningMultivariate Time Series Classification with Hierarchical Variational Graph Pooling
With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC techniques, which mostly rely on convolutional or…
ClassificationDecoderGeneral ClassificationGraph Classification+6Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. We find that real dynamic attribute…
AttributeGraph Neural NetworkGraph structure learningLink PredictionNode Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representati…
General ClassificationGraph structure learningMeta-LearningNetwork Embedding+2Graph Structure Learning for Robust Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial att…
Graph Neural NetworkGraph structure learningRepresentation LearningReasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoni…
Graph LearningGraph structure learningTransfer LearningVisual DialogSpatio-Temporal Graph Structure Learning for Traffic Forecasting
As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting inherently subjects to the following three challenging aspects. First, traffic data are physically associated with road networ…
Graph structure learningTime SeriesTime Series AnalysisTraffic PredictionGraph-Bert: Only Attention is Needed for Learning Graph Representations
The dominant graph neural networks (GNNs) over-rely on the graph links, several serious performance problems with which have been witnessed already, e.g., suspended animation problem and over-smoothing problem. What's mo…
AttributeClusteringGraph ClusteringGraph Neural Network+2Deep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneo…
Graph LearningGraph structure learningInductive LearningMetric Learning+1Variationally Regularized Graph-based Representation Learning for Electronic Health Records
Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of diseases and lab-disease correlations can be …
DecoderGraph Neural NetworkGraph structure learningRepresentation LearningLarge-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points
We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack of labeled data and the availability of l…
Graph structure learningGraph Generation with Variational Recurrent Neural Network
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurrent Neural Network (GraphVRNN), a probabil…
AttributeGraph GenerationGraph structure learningIterative Deep Graph Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embedding simultaneously. We first cast graph structure learni…
Graph EmbeddingGraph LearningGraph structure learningMetric LearningGraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effect…
Graph Neural NetworkGraph structure learningMachine Reading ComprehensionReading Comprehension