The information and opinions expressed below are based on my analysis of price behavior and chart activity. Tuesday, June 3, ...

Subgraph-based methods have been introduced as solutions but often ignore shared information among subgraphs. To address these limitations, we propose a Subgraph-aware Graph Kernel Neural Network ...

A research team from Kumamoto University has developed a promising deep learning model that significantly enhances the accuracy of subgraph matching—a critical task in fields ranging from drug ...

Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured ...

Abstract: Subgraph querying has been applied in a variety of emerging applications. Traditional subgraph querying based on subgraph isomorphism requires identical label matching, which is often too ...

(NASDAQ: MLGO), (the "Company"or "MicroAlgo"), today announced the introduction of an innovative solution: a multi-simulator collaborative algorithm based on subgraph isomorphism, aimed at ...

The repo lists papers of various areas, including Graph Analytics, Graph Acceleration, Subgraph Matching, Streaming Graph Management, Worst-case Optimal Join Algorithms, Join Plan Optimization, and so ...

Specifically, subgraph search query is one of the most popular query paradigms for accessing graph data. Since graphs are intuitive to draw, graph data management tools from academia and industry (for ...

This announcement comes after subgraph protocol developer The Graph stated in June that it would be shutting down its hosted service in favor of a decentralized network. Subgraphs are application ...