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 ...

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...

They require a knowledge graph. How does the journey to a knowledge graph start with unstructured data—such as text, images, and other media? The evolution of web search engines offers an ...

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 ...

The algorithm works in an abstracted road map called a graph: a network of interconnected points (called vertices) in which the links between vertices are labeled with numbers (called weights). These ...

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 ...

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 ...