Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Dataset Condensation with Latent Quantile Matching
Dataset condensation (DC) methods aim to learn a smaller synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn …
Dataset CondensationGraph LearningTowards Neural Scaling Laws for Foundation Models on Temporal Graphs
The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observed temporal graphs, is it possible to predict the evolution of an unseen network …
Graph LearningGraph Property PredictionProperty PredictionSelf-supervised Graph Neural Network for Mechanical CAD Retrieval
CAD (Computer-Aided Design) plays a crucial role in mechanical industry, where large numbers of similar-shaped CAD parts are often created. Efficiently reusing these parts is key to reducing design and production costs f…
3D Shape RepresentationGraph LearningGraph Neural NetworkRepresentation Learning+1A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
Subgraph Graph Neural Networks (Subgraph GNNs) enhance the expressivity of message-passing GNNs by representing graphs as sets of subgraphs. They have shown impressive performance on several tasks, but their complexity l…
Graph LearningScalable and Flexible Causal Discovery with an Efficient Test for Adjacency
To make accurate predictions, understand mechanisms, and design interventions in systems of many variables, we wish to learn causal graphs from large scale data. Unfortunately the space of all possible causal graphs is e…
Causal DiscoveryGraph LearningSchur's Positive-Definite Network: Deep Learning in the SPD cone with structure
Estimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various convex optimization-based estimators, they …
Graph LearningInductive BiasGraph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks
Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities for tasks such as social networks and medical data analysis. Despite their successes…
Graph LearningInductive LearningTransductive LearningGPT4Rec: Graph Prompt Tuning for Streaming Recommendation
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional models, typically reliant on a static t…
Graph LearningRecommendation SystemsHeuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction
Link prediction is a fundamental task in graph learning, inherently shaped by the topology of the graph. While traditional heuristics are grounded in graph topology, they encounter challenges in generalizing across diver…
Graph LearningGraph Neural NetworkLink PredictionOn the Hölder Stability of Multiset and Graph Neural Networks
Extensive research efforts have been put into characterizing and constructing maximally separating multiset and graph neural networks. However, recent empirical evidence suggests the notion of separation itself doesn't c…
Graph LearningGKAN: Graph Kolmogorov-Arnold Networks
We introduce Graph Kolmogorov-Arnold Networks (GKAN), an innovative neural network architecture that extends the principles of the recently proposed Kolmogorov-Arnold Networks (KAN) to graph-structured data. By adopting …
Graph LearningKolmogorov-Arnold NetworksAsync Learned User Embeddings for Ads Delivery Optimization
In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance. The effectiveness of reco…
Graph LearningRecommendation SystemsRetrievalGraph Mining under Data scarcity
Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning for graphs has been introduced to overc…
ClassificationFew-Shot LearningGraph LearningGraph Mining+2From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning
Dynamic link prediction is an important problem considered by many recent works proposing various approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on publicly available bench…
Dynamic Link PredictionGraph LearningLink PredictionPredictionHigher-order Structure Based Anomaly Detection on Attributed Networks
Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can …
Anomaly DetectionAttributeFraud DetectionGraph Attention+2A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Graph Neural Networks (GNNs) extend convolutional neural networks to operate on graphs. Despite their impressive performances in various graph learning tasks, the theoretical understanding of their generalization capabil…
Generalization BoundsGraph LearningPANDORA: Deep graph learning based COVID-19 infection risk level forecasting
COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elements of society must deliver measurable actions based on the pandemic's se…
Graph LearningInterpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
We build interpretable and lightweight transformer-like neural networks by unrolling iterative optimization algorithms that minimize graph smoothness priors -- the quadratic graph Laplacian regularizer (GLR) and the $\el…
Graph LearningInformed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informativ…
DenoisingGraph LearningCombinatorial Optimization with Automated Graph Neural Networks
In recent years, graph neural networks (GNNs) have become increasingly popular for solving NP-hard combinatorial optimization (CO) problems, such as maximum cut and maximum independent set. The core idea behind these met…
Combinatorial OptimizationGraph EmbeddingGraph LearningNeural Architecture Search