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.

We designed a graph-informed convolutional autoencoder called GICA to extract high-level ... typically for tasks such as data compression or dimensionality reduction. However, other neural network ...

Knowledge graphs have existed for a long time and have proven valuable across social media sites, cultural heritage institutions, and other enterprises. A knowledge graph is a collection of ...

To address the uncertainties in communication time and edge servers’ available capacity, we propose a novel semantic compression method ... To address this maximization problem, we propose a graph ...

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent ...

Google introduced the Knowledge Graph in 2012 to help searchers discover new information quicker. Essentially, users can search for places, people, companies, and products and find instant results ...

Abstract: This work aims to propose a novel architecture and training strategy for graph convolutional networks (GCN). The proposed architecture, named Autoencoder-Aided GCN (AA-GCN), compresses the ...

Opinions expressed are those of the author. Graph technology has become a requirement for the modern enterprise. Companies in virtually every industry, from healthcare to energy to financial ...

Advances in graph embedding techniques have enabled automatically ... Although Autoencoder has the same input and output, it also has a certain degree of loss, so autoencoder is also called lossy ...