Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation
Multimodal recommendation systems can learn users' preferences from existing user-item interactions as well as the semantics of multimodal data associated with items. Many existing methods model this through a multimodal…
Graph LearningMulti-modal RecommendationMultimodal RecommendationRecommendation SystemsGraph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration of Graph Neural Networks (GNNs) and Larg…
Domain GeneralizationGraph LearningGraph Question AnsweringQuestion Answering+2Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks
With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samp…
Graph LearningIntrusion DetectionSelf-Supervised LearningSPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning
Session-based recommendation seeks to forecast the next item a user will be interested in, based on their interaction sequences. Due to limited interaction data, session-based recommendation faces the challenge of limite…
Graph LearningSession-Based RecommendationsSemi-Implicit Neural Ordinary Differential Equations
Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in graph learning and scientific machine l…
Graph ClassificationGraph LearningMulti-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespread popularity and application across var…
Community Question AnsweringGraph LearningQuestion AnsweringMOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation
The prevalence of unhealthy eating habits has become an increasingly concerning issue in the United States. However, major food recommendation platforms (e.g., Yelp) continue to prioritize users' dietary preferences over…
DescriptiveFood recommendationGraph LearningLarge Language Model+1Towards joint graph learning and sampling set selection from data
We explore the problem of sampling graph signals in scenarios where the graph structure is not predefined and must be inferred from data. In this scenario, existing approaches rely on a two-step process, where a graph is…
Graph LearningGraph SamplingGLL: A Differentiable Graph Learning Layer for Neural Networks
Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods fail to leverage the relational informati…
Graph LearningFast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks
Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when applied to large-scale graphs. To addre…
DenoisingGraph LearningAHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks
Adversarial attacks on Graph Neural Networks aim to perturb the performance of the learner by carefully modifying the graph topology and node attributes. Existing methods achieve attack stealthiness by constraining the m…
Adversarial AttackGraph LearningTowards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learning tasks. However, the general applicab…
BenchmarkingGraph LearningMy Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis
In implicit emotion analysis (IEA), the subtlety of emotional expressions makes it particularly sensitive to user-specific characteristics. Existing studies often inject personalization into the analysis by focusing on t…
Emotion RecognitionGraph LearningLarge Language ModelPix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery
Extraction of building footprint polygons from remotely sensed data is essential for several urban understanding tasks such as reconstruction, navigation, and mapping. Despite significant progress in the area, extracting…
DecoderExtracting Buildings In Remote Sensing ImagesGraph LearningRoad SegmentationAdaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation
Surgical workflow anticipation is the task of predicting the timing of relevant surgical events from live video data, which is critical in Robotic-Assisted Surgery (RAS). Accurate predictions require the use of spatial i…
Graph LearningRevisiting the Necessity of Graph Learning and Common Graph Benchmarks
Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in large scale industrial settings. Tacitl…
Graph LearningFragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal Information
Automatically constructing GUI groups of different granularities constitutes a critical intelligent step towards automating GUI design and implementation tasks. Specifically, in the industrial GUI-to-code process, fragme…
Graph LearningGraph Neural NetworkTraining MLPs on Graphs without Supervision
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive app…
Fraud DetectionGraph ClassificationGraph LearningInductive Bias+3Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks…
Contrastive LearningGraph LearningStock Trend PredictionNode Classification With Integrated Reject Option
One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not previously explored. In this paper, we …
ClassificationGraph LearningNode Classification