Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
T2T-LA: A Topology-to-Topology LLM Agent for Graph Learning with Neither Feature Access nor Task Knowledge
Graph learning aims to convert data into graph representations, which are fundamental to many problems in machine learning for CAD, where circuits, layouts, designs, and optimization states are often modeled as graph-str…
Graph Representation LearningGraph LearningAdversarial Signed Graph Learning with Differential Privacy
Signed graphs with positive and negative edges can model complex relationships in social networks. Leveraging on balance theory that deduces edge signs from multi-hop node pairs, signed graph learning can generate node e…
Graph LearningGFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion
3D face recognition offers a robust biometric solution by capturing facial geometry, providing resilience to variations in illumination, pose changes, and presentation attacks. Its strong spoof resistance makes it suitab…
Face RecognitionGraph LearningDecoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensation
In multimodal graph learning, graph structures that integrate information from multiple sources, such as vision and text, can more comprehensively model complex entity relationships. However, the continuous growth of the…
Graph LearningRepresentation Integrity in Temporal Graph Learning Methods
Real-world systems ranging from airline routes to cryptocurrency transfers are naturally modelled as dynamic graphs whose topology changes over time. Conventional benchmarks judge dynamic-graph learners by a handful of t…
Graph LearningGRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction
Graph transformers have demonstrated remarkable capability on complex spatio-temporal tasks, yet their depth is often limited by oversmoothing and weak long-range dependency modeling. To address these challenges, we intr…
Graph LearningRobust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
Recently, fine-tuning large-scale pre-trained GNNs has yielded remarkable attention in adapting pre-trained GNN models for downstream graph learning tasks. One representative fine-tuning method is to exploit adapter (ter…
Graph LearningGROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gained popularity as a potential solution to…
graph partitioningGraph LearningTowards Efficient LLM-aware Heterogeneous Graph Learning
Heterogeneous graphs are widely present in real-world complex networks, where the diversity of node and relation types leads to complex and rich semantics. Efforts for modeling complex relation semantics in heterogeneous…
Graph LearningA Fast Binary Splitting Approach for Non-Adaptive Learning of Erdős--Rényi Graphs
We study the problem of learning an unknown graph via group queries on node subsets, where each query reports whether at least one edge is present among the queried nodes. In general, learning arbitrary graphs with $n$ n…
Graph LearningWhen Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected
Graphs provide a unified representation of semantic content and relational structure, making them a natural fit for domains such as molecular modeling, citation networks, and social graphs. Meanwhile, large language mode…
Graph LearningD2D Power Allocation via Quantum Graph Neural Network
Increasing wireless network complexity demands scalable resource management. Classical GNNs excel at graph learning but incur high computational costs in large-scale settings. We present a fully quantum Graph Neural Netw…
Graph Neural NetworkGraph LearningIntegrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
Deep graph learning has advanced Alzheimer's (AD) disease classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease specific features. We present Causal-GCN, …
Causal InferenceGraph LearningSelf-Adaptive Graph Mixture of Models
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to plateau. In many cases, well-established mo…
Graph ClassificationNode ClassificationLink PredictionGraph LearningFairness-Aware Graph Representation Learning with Limited Demographic Information
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning methods have been proposed in recent years. …
Graph Representation LearningGraph LearningGlobal-Lens Transformers: Adaptive Token Mixing for Dynamic Link Prediction
Dynamic graph learning plays a pivotal role in modeling evolving relationships over time, especially for temporal link prediction tasks in domains such as traffic systems, social networks, and recommendation platforms. W…
Dynamic Link PredictionGraph LearningEcholess Label-Based Pre-computation for Memory-Efficient Heterogeneous Graph Learning
Heterogeneous Graph Neural Networks (HGNNs) are widely used for deep learning on heterogeneous graphs. Typical end-to-end HGNNs require repetitive message passing during training, limiting efficiency for large-scale real…
Graph LearningDynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss
The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approache…
Incomplete multi-view clusteringContrastive LearningGraph LearningHeterogeneous Attributed Graph Learning via Neighborhood-Aware Star Kernels
Attributed graphs, typically characterized by irregular topologies and a mix of numerical and categorical attributes, are ubiquitous in diverse domains such as social networks, bioinformatics, and cheminformatics. While …
Graph SimilarityGraph LearningMP-GFormer: A 3D-Geometry-Aware Dynamic Graph Transformer Approach for Machining Process Planning
Machining process planning (MP) is inherently complex due to structural and geometrical dependencies among part features and machining operations. A key challenge lies in capturing dynamic interdependencies that evolve w…
Graph Learning