This fusion mechanism effectively enhances the model’s ability to learn node representations, especially in complex graph structures, with accuracy improvements of 1%-2% compared to GCN and GraphSAGE.

Please view our affiliate disclosure. Trading involves risk which may result in the loss of capital. The Graph offers developers an easy-to-use, cost-efficient, and secure API. This network allows ...

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our ...

Abstract: Contrastive graph ... cumulative contrastive loss function is designed to characterize the structural information by dynamically accumulating the influence of the nodes with different hops.

However, domain-specific approaches for extracting knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to ...

Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network ... provides a more comprehensive and effective ...

The Microsoft Graph Core Python Client Library contains core classes used by Microsoft Graph Python Client Library to send native HTTP requests to Microsoft Graph API. To call Microsoft Graph, your ...

In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution ...