Abstract: Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that ...

Graphene is a two-dimensional material consisting of a single layer of carbon atoms arranged in a honeycomb structure. Its properties include high strength and good conductivity of heat and ...

Abstract: This paper investigates a GraphRAG framework that integrates knowledge graphs into the Retrieval-Augmented Generation (RAG) architecture to enhance networking applications. While RAG has ...

The Graph offers access to competitive and cost-efficient decentralized data sets. The network boasts a 99.99% uptime and 24/7 availability. Central to The Graph’s operations are subgraphs, APIs that ...

GraphSAINT is a general and flexible framework for training GNNs on large graphs. GraphSAINT highlights a novel minibatch method specifically optimized for data with complex relationships (i.e., ...

This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph, as described in our paper: Thomas N. Kipf, Max Welling, ...

What began with a focus on weather forecasting has evolved toward addressing errors in scientific modeling. In the collaborative environment of the Penn State Institute for Computational and Data ...

StepFun has introduced Step-DeepResearch, a 32B parameter end to end deep research agent that aims to turn web search into actual research workflows with ...

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