As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...

Broadcom is proving to be a key beneficiary in the AI networking market, with around $20 billion of non-accelerator AI backlog. Coherent is also benefiting from the ongoing AI networking upgrade cycle ...

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...

One of the most complex quantum networks built to date would allow 18 people to communicate securely thanks to the power of quantum physics. The researchers behind the work say it offers a practical ...

This report, commissioned by TRON and produced by CoinDesk Research, provides an update on the TRON blockchain for Q3 2025. The report is intended to support institutional allocators, developers, and ...

In the documentation, there is no example of using something else than models.Ellipse. But it would be very useful to use other shapes, for example model.Block or model.Quad. In addition, the current ...

Dubai, August 18, 2025 – Truth Network, a decentralized infrastructure protocol supporting global prediction markets, announced today that it has surpassed 19,000 node allocations, marking a ...

How can we make every node in a graph its own intelligent agent—capable of personalized reasoning, adaptive retrieval, and autonomous decision-making? This is the core question explored by a group ...