그래프 튜토리얼 3화 : Graph Node Sampling

26년 2월 1주차 그래프 오마카세

Learning Topology and Physical Laws Beyond Nodes and Edges : E(n)-Equivariant Topological Neural Networks E(n) Equivariant Topological Neural NetworksGraph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing

26년 1월 4주차 그래프 오마카

The Synergy Created By MCP, and Knowledge Graph of Agentic AI Memgraph blogStay up-to-date with the latest trends and insights in graph database technology with Memgraph’s blog.GraphRAG & Knowledge Graphs: Making Your Data AI-Ready for 2026 - FlureeDiscover why 78% of companies aren’t AI-ready and how GraphRAG

26년 1월 3주차 그래프 오마카세

GUG Interview 안녕하세요, 정이태입니다. GUG talks 소식을 전해드립니다. 이번 세션은 Neo4j 솔루션 엔지니어 Bryan Lee 함께 점심시간을 활용해 온라인으로 진행합니다. 가볍게 점심을 준비해 편안한 마음으로 참여해 주시면 좋을 것 같습니다. Bryan disucssion topic 최근 지식 그래프(Knowledge Graph) 구축을 위한 NER(Named Entity Recognition) 태스크에서 다양한 연구와 시행착오를 겪은 Bryan의

26년 1월 2주차 그래프 오마카세

mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural NetworksGraph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test. We adapt Manifold-Constrained Hyper-Connections (\mhc)~\citep{xie2025mhc}, recently proposed for Transformers, to graph neural networks. Our method, mHC-GNN, expands