This is the implementation of the paper Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters. cd ~; git clone https://github.com/NVIDIA/apex cd ...

These systems aim to improve the scalability and efficiency of processing large graphs, which often have irregular structures and high volumes of data. A survey of current graph partitioning ...

These are the products that I’d want with me on a deserted island (cuz I’d obviously have lots of time for watercolor and ...

By deriving lower bounds from tree metrics, this approach allows for effective indexing and faster query responses in large graph databases, which is particularly beneficial for applications ...

Current summarization approaches mainly apply single strategies such as graph structure, pattern mining or relevance metrics to calculate RDFG summaries. Different to the existing approaches, this ...

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...

Extensive experiments on benchmark datasets from small to large demonstrate the superiority of NAGphormer+ against existing graph Transformers and mainstream GNNs, as well as the original NAGphormer.

Now, Barthelmey has taken things one step further by publishing a new paper in the journal Physical Review E that treats ...

Despite recent advancements, many graph generative models still rely heavily on adjacency matrix representations. While effective, these methods can be computationally demanding and often lack ...

The Graph price prediction anticipates a high of $0.419 by the end of 2025. In 2028, it will range between $0.978 and $1.12, with an average price of $1.05. In 2031, it will range between $1.68 and $1 ...

In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the ...