Abstract: In recent years, reconstructing features and learning node representations by graph autoencoders (GAE) have attracted much attention in deep graph node clustering. However, existing works ...

The Graph’s creator team, Edge & Node, has formally joined the LF Decentralized Trust, a collaborative project under the Linux Foundation centered on the open development of standards and ...

Cybersecurity researchers have disclosed details of an active malware campaign called Stealit that has leveraged Node.js' Single Executable Application (SEA) feature as a way to distribute its ...

Currently each input and output in a nodegraph is their own separate node. This can complicate nodegraphs with many inputs or outputs. It would be interesting to instead have a single "input" node ...

Since October 2024, Microsoft Defender Experts (DEX) has observed and helped multiple customers address campaigns leveraging Node.js to deliver malware and other payloads that ultimately lead to ...

Abstract: Graph Edit Distance (GED) is a classical graph similarity metric. Since exact GED computation is NP-hard, existing GNN-based methods try to approximate GED in polynomial time. However, they ...

Have you ever found yourself tangled in a web of overly complicated workflows in Bricksforge, struggling to keep track of what triggers what, and why? But what if there was a way to make it simpler, ...

In today’s world, Graph similarity computation (GSC) plays an important role in various applications such as code detection, molecular graph similarity, image matching, etc., by evaluating the ...

We are a weekly podcast and newsletter made to deliver... Google’s proposal aims to split JavaScript into two parts: JS0, the core language that engines implement, and JSSugar, extra features that ...

If you use Node.js for web development, creation of web apps, and javascript based network applications, you undoubtedly may come across situations where you’d like to make sure that the latest ...

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph ...