Abstract: Understanding the underlying graph structure of a nonlinear map over a particular domain is essential in evaluating its potential for real applications. In this paper, we investigate the ...
I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...
Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...
The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...
As enterprises continue to invest heavily in advanced analytics and large language models (LLMs), graph technology has become one of the most favored approaches for setting up the data stack. It ...
Abstract: In light of the growing emphasis on the right to be forgotten of graph data, machine unlearning has been extended to unlearn the graph structures’ knowledge from graph neural networks (GNNs) ...
[KDD 2024] In this repository, we present the code of Graph Data Condensation via Self-expressive Graph Structure Reconstruction (GCSR). python==3.7.13 torch==1.13.0 torchvision==0.14.0 ...
This is the implementation of the Graph Attention Structure-from-Motion (GASFM) architecture, presented in our CVPR 2024 paper Learning Structure-from-Motion with Graph Attention Networks. The ...
In the vast world of artificial intelligence, developers face a common challenge – ensuring the reliability and quality of outputs generated by large language models (LLMs). The outputs, like ...
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