GraphOmakase
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
GraphOmakase
Beyond Context Graphs: Why 2026 Must Be the Year of Agentic Memory, Causality, and Explainability https://medium.com/@volodymyrpavlyshyn/beyond-context-graphs-why-2026-must-be-the-year-of-agentic-memory-causality-and-explainability-db43632dbdee * 안녕하세요, 구독자 여러분. 희망찬 2026년 새해가 밝았습니다. 새해 복 많이 받으시고, 좋은 일들로 가득한 한 해가 되시기를 바랍니다. * 2026년의 첫 오마카세로 어떤 것이 좋을지 여러 아티클들을 찾아보고 읽어보다가, "요즘
GraphOmakase
Signals with shape: why topology matters for modern data? News article: Signals with shape: why topology matters for modern data?SURE-AI * 어느덧 2025년 을사년의 마지막 그래프 오마카세로 인사드리게 되었습니다. 구독자 여러분들의 올 해는 어떠셨을까요? 각자의 현장에서 혁신과 큰 발전을 이끌어오셨을 구독자 여러분들께 안부를 전합니다. 지금 한국은 엄청난 한파라고 들었습니다만 모두
Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on Math Textbook Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on Math TextbookTechnology-enhanced learning environments often help students retrieve relevant learning content for questions arising during self-paced study. Large language models (LLMs) have emerged as novel aids for information
GraphOmakase
Learning to Retrieve and Reason on Knowledge Graph through Active Self-Reflection Learning to Retrieve and Reason on Knowledge Graph through Active Self-ReflectionExtensive research has investigated the integration of large language models (LLMs) with knowledge graphs to enhance the reasoning process. However, understanding how models perform reasoning utilizing structured graph knowledge
GraphOmakase
In-depth Analysis of Graph-based RAG in a Unified Framework In-depth Analysis of Graph-based RAG in a Unified FrameworkGraph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptability, interpretability, and trustworthiness. A number of graph-based RAG methods have been proposed
GraphOmakase
Why Ontologies are Key for Data Governance in the LLM Era? Reference Blog : https://medium.com/timbr-ai * 최근 LLM 도입을 검토하는 많은 기업 사이에서 온톨로지(Ontology)가 뜨거운 화두로 떠오르고 있습니다. 다들 잘 아시다시피, 온톨로지란 쉽게 말해 데이터 간의 관계와 의미를 정의하는 지도로 받아들일 수 있습니다. 단순히 데이터를 저장하는 것을
GraphOmakase
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes NodeRAG: Structuring Graph-based RAG with Heterogeneous NodesRetrieval-augmented generation (RAG) empowers large language models to access external and private corpus, enabling factually consistent responses in specific domains. By exploiting the inherent structure of the corpus, graph-based RAG methods further enrich this process by building
GraphOmakase
When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented GenerationGraph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge. It leverages graphs to model the
GraphOmakase
GraphFrames: Architectural Evolution from GraphX for Big Data and AI Applications GraphFrames: an integrated API for mixing graph and relational queriesGraph data is prevalent in many domains, but it has usually required specialized engines to analyze. This design is onerous for users and precludes optimization across complete workflows. We…OpenReview.
GraphOmakase
Verifying Chain-of-Thought Reasoning via Its Computational Graph Verifying Chain-of-Thought Reasoning via Its Computational GraphCurrent Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a computation fails. We introduce a white-box method: Circuit-based Reasoning Verification (CRV). We hypothesize that attribution
GraphOmakase
Introducing GraphQA: An Agent for Asking Graphs Questions Introducing GraphQA: An Agent for Asking Graphs Questions | CatioGraphQA is Catio’s new open-source agent for natural-language questions over architecture graphs, fusing LLMs with graph algorithms to deliver fast, structure-aware answers for dependencies, flows, and system reasoning.catio-logo-blueIman MakaremiGitHub - catio-tech/graphqa: