Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
The Logical Expressiveness of Topological Neural Networks
Graph neural networks (GNNs) are the standard for learning on graphs, yet they have limited expressive power, often expressed in terms of the Weisfeiler-Leman (WL) hierarchy or within the framework of first-order logic. …
Graph Representation LearningTACENR: Task-Agnostic Contrastive Explanations for Node Representations
Graph representation learning has achieved notable success in encoding graph-structured data into latent vector spaces, enabling a wide range of downstream tasks. However, these node representations remain opaque and dif…
Graph Representation LearningContrastive LearningContraction and Hourglass Persistence for Learning on Graphs, Simplices, and Cells
Persistent homology (PH) encodes global information, such as cycles, and is thus increasingly integrated into graph neural networks (GNNs). PH methods in GNNs typically traverse an increasing sequence of subgraphs. In th…
Graph Representation LearningA Closer Look at the Application of Causal Inference in Graph Representation Learning
Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to identify causal subgraphs or mitigate confou…
Graph Representation LearningCausal InferenceGraph LearningGraph Topology Information Enhanced Heterogeneous Graph Representation Learning
Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph S…
Graph Representation LearningGraph structure learningFeature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends on high-fidelity sensor streams containing proprietary process information, limiting collabo…
Graph Representation LearningFeature ImportanceCrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-world settings with shared schemas and f…
parameter-efficient fine-tuningGraph Representation LearningDomain GeneralizationGraph ClassificationGeometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow
We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning through explicit modeling of geometric evolution on graph structures. Specifically, …
Graph Representation LearningKCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuits
Digital circuits representation learning has made remarkable progress in the electronic design automation domain, effectively supporting critical tasks such as testability analysis and logic reasoning. However, represent…
Graph Representation LearningGraph Neural NetworkCausality-Driven Disentangled Representation Learning in Multiplex Graphs
Learning representations from multiplex graphs, i.e., multi-layer networks where nodes interact through multiple relation types, is challenging due to the entanglement of shared (common) and layer-specific (private) info…
Graph Representation LearningCausal InferenceGeometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a single Riemannian manifold. While emerging mixed-curvature methods attempt to capture such dive…
Graph Representation LearningMultimodal Graph Representation Learning with Dynamic Information Pathways
Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requires both adaptive intra-modal message pa…
Graph Representation LearningNode ClassificationLink PredictionGraph LearningBAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop
The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient due to the extensive expert knowledge requ…
Graph Representation LearningGraph Neural NetworkGraph LearningMUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
Universal graph pre-training has emerged as a key paradigm in graph representation learning, offering a promising way to train encoders to learn transferable representations from unlabeled graphs and to effectively gener…
Graph Representation LearningVecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention
Graph Transformer has demonstrated impressive capabilities in the field of graph representation learning. However, existing approaches face two critical challenges: (1) most models suffer from exponentially increasing co…
Graph Representation LearningNode ClassificationRandom Wavelet Features for Graph Kernel Machines
Node embeddings map graph vertices into low-dimensional Euclidean spaces while preserving structural information. They are central to tasks such as node classification, link prediction, and signal reconstruction. A key g…
Graph Representation LearningNode ClassificationLink PredictionRiemannGL: Riemannian Geometry Changes Graph Deep Learning
Graphs are ubiquitous, and learning on graphs has become a cornerstone in artificial intelligence and data mining communities. Unlike pixel grids in images or sequential structures in language, graphs exhibit a typical n…
Graph Representation LearningGraph LearningHomophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and graph representation learning. However, they remain susceptible to biases that can…
Graph Representation LearningGraph Neural NetworkNode ClassificationLink PredictionUniTrack: Differentiable Graph Representation Learning for Multi-Object Tracking
We present UniTrack, a plug-and-play graph-theoretic loss function designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified differentia…
Graph Representation LearningMulti-Object TrackingChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs
Dynamic graph representation learning requires capturing both structural relations and temporal evolution, yet existing approaches face a core trade-off: attention-based methods offer expressiveness at $O(T^2)$ complexit…
Graph Representation LearningGraph Neural Network