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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
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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:
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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.
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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
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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
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Position: Message-passing and spectral GNNs are two sides of the same coin paper link : https://arxiv.org/abs/2602.10031 Keywords * MPNN, Spectral GNN * Graph Shift Operator * Spectral PE * Roadmap and Vision * 새로운 GNN 프로젝트를 위한 적절한 아키텍처들을 구축하고자 할 때 자연스럽게 두 가지 표준적인 접근 방식 사이에서 고민하게 됩니다.
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RAG-ANYTHING: ALL-IN-ONE RAG FRAMEWORK RAG-Anything: All-in-One RAG FrameworkRetrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalignment exists between current RAG capabilities and real-world information environments. Modern knowledge repositories are inherently multimodal, containing rich combinations of textual
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Graph Talks LLM,GNN integration 행사 참여 신청 링크 안녕하세요, GUG 정이태입니다. 1.입춘이 다가오고 있지만 여전히 바람이 매섭네요. 다행히 지난 주말은 바람이 잦아들어 잠시나마 숨을 돌릴 수 있는 휴일이었는데, 다들 건강하게 잘 지내고 계신가요? 2.요즘 AI 업계는 NVIDIA GTC 2025에서 화두가 되었던 GraphRAG(Softprompt, KGE)를 넘어, 다가올
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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
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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
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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
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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