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
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
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.
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
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:
GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial DataAlthough data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property prediction only cover a surprisingly narrow set
Flexible GraphRAG: a configurable open source framework for GraphRAG Blog link : https://integratedsemantics.org/ Github : https://github.com/stevereiner/flexible-graphrag * 이번 주 오마카세는 'Open Source Integrated AI and Semantic Tech' 블로그에서 설명하는 통합된 오픈소스 플랫폼 관련한 소식을 전달해드리고자 합니다. 최근 포스트에서는 문서 처리, 지식 그래프 구축, RAG 및
1.광고 안녕하세요 정이태입니다.오랜만에 10월 23일 목요일 저녁에 GUG 온라인 세미나가 예정되어 있습니다. 카이스트 김재철AI대학원 신기정 교수님 연구실 소속 김선우 연구원님과 하이퍼 그래프 이론부터 응용까지 라는 주제로 진행할 예정이며, 저또한 모더레이터 겸 요새 GraphRAG 트랜드부터 왜 HyperGraphRAG가 나오고 있는지 산업계 관점에서 생각을 공유드릴 예정이오니 관심있는 분들은 참여하셔서 본인 연구나
Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph PropertiesRecent large-scale reasoning models have achieved state-of-the-art performance on challenging mathematical benchmarks, yet the internal mechanisms underlying their success remain poorly understood. In this work, we introduce the notion
Are Large Language Models Good Temporal Graph Learners? Youtube link : https://www.youtube.com/watch?v=jmCwOQX9Ank * 이번 주 오마카세는 TGL (Temporal Graph Learning) 세미나에서 대규모 언어 모델(LLM)이 관계형 데이터베이스에서 어떻게 딥러닝을 수행하는지에 초점을 맞추어 발표된, 기존의 시계열 그래프 학습 방법론에 LLM을 접목하여 새로운 가능성을 탐색한 '대규모
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned ExpertsGraph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning architectures that generalize to
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementLong-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks,