Building the Virtuous Cycle of Graph Knowledge

26년 6월 2주차 그래프 오마카세

Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node ClassificationarXiv logoGitHub - transductive-sharpening/tunedGNN: Transductive Sharpening applied on top of the TunedGNN baseline.Transductive Sharpening applied on top of the TunedGNN baseline. - transductive-sharpening/tunedGNNGitHubtransductive-sharpening Keywords * Transductive Learning * Semi-supervised GNN * Tsallis E

26년 6월 1주차 그래프 오마카세

Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural RefinementSensor networks increasingly govern modern infrastructure, yet the data they lose are rarely missing in the uniform-random patterns assumed by standard imputation benchmarks. Loop detectors

26년 5월 4주차 그래프 오마카세

Random Sets Graph Neural Networks Random-Set Graph Neural NetworksUncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite their predictive capabilities being ever useful across industrial workspaces, the inherent uncertainty induced by the nature of the data is a huge mitigating

26년 5월 3주차 그래프 오마카세

Root-to-Leaf Path Random Walks, Normalized Hodge Laplacians, and Cheeger Inequalities on Simplicial Complexes Root-to-Leaf Path Random Walks, Normalized Hodge Laplacians, and Cheeger Inequalities on Simplicial ComplexesWe introduce root-to-leaf path random walks on double covers of graded signed graphs and analyze their behavior in a general setting. Viewing simplicial complexes within

26년 5월 2주차 그래프 오마카세

Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive IntegrationGraph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often challenged by navigating large-scale hierarchical

26년 5월 1주차 그래프 오마카세

Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning Topology-Aware Representation Alignment for Semi-Supervised Vision-Language LearningVision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language learning mitigates this limitation by leveraging a small set of labeled image-text pairs together with abundant unlabeled images, existing methods

GUG 소식

안녕하세요 GUG 정이태입니다. 2026 1분기가 지나간지가 엊그제 같은데 벌써 2분기의 시작인 4월이 끝나가고 있네요. 연초에 세우셨던 계획들은 잘 수행하고 계시는지요. GUG 는 여러분들의 성원 덕분에 꾸준히 번창하고 있습니다. 지금 글을 작성하고 있는 26년 4월 27일 기준으로 857명의 구독자분들이 함께 해주고 계십니다. 늘 감사드립니다. 요새 바쁘다는 핑계로 GUG 신경을 못쓰고 있었는데,

26년 4월 3주차 그래프 오마카세

Learning Laplacian Forms for Graph Signal Processing via the Deformed Laplacian Learning Laplacian Forms for Graph Signal Processing via the Deformed LaplacianLearning the graph Laplacian from observed data is one of the most investigated and fundamental tasks in Graph Signal Processing (GSP). Different variants of the Laplacian, such as the

26년 4월 2주차 그래프 오마카세

GraphFrames, a major graph analysis library update New GraphFrames release: Improved performance, new algorithms, and documentation | Sem Sinchenko posted on the topic | LinkedInOn behalf of the GraphFrames maintainers, I am happy to announce the delivery of a new release. It is a significant improvement! It improves performance and memory management:

26년 4월 1주차 그래프 오마카세

PolyGraph Discrepancy: a classifier-based metric for graph generation PolyGraph Discrepancy: a classifier-based metric for graph generationExisting methods for evaluating graph generative models primarily rely on Maximum Mean Discrepancy (MMD) metrics based on graph descriptors. While these metrics can rank generative models, they do not provide an absolute measure of performance.

26년 3월 4주차 그래프 오마카세

Graphs are maximally expressive for higher-order interactions Graphs are maximally expressive for higher-order interactionsWe demonstrate that graph-based models are fully capable of representing higher-order interactions, and have a long history of being used for precisely this purpose. This stands in contrast to a common claim in the recent literature on

26년 3월 3주차 그래프 오마카세

A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware paper link : https://arxiv.org/abs/2601.16118 Keywords * Spiking Neural Network (SNN) * Neuromorphic Hardware * Hypergraph * Graph partitioning * LLM 기반 거대 AI 인프라가 수천 대의 서버로 구성된 대규모 데이터센터를 필요로 하는 것과 대조적으로, 인간의 뇌는 놀라울 정도로 높은