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Graph Product Reviews: How We Test Tools
GraphUserGroup reviews graph products from a user perspective, including setup, cost, limits, and real use cases.
SEOCHO: Open-Source Graph Memory for Agents
SEOCHO is GraphUserGroup's open-source track for ontology-aligned graph memory, GraphRAG, and agent development.
26년 6월 4주차 그래프 오마카세
Topological Neural Operator Topological Neural OperatorsWe introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological domains. TNOs represent data as features defined on cells of varying dimension and model their interactions
26년 6월 3주차 그래프 오마카세
AI Must Embrace Specialization via Superhuman Adaptable Intelligence AI Must Embrace Specialization via Superhuman Adaptable IntelligenceEveryone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don’t seem to agree on its exact definition. One common definition of AGI
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
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
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
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
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
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
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,