To address this issue, this paper introduces a hierarchical multi-relational graph representation learning (HMGRL) approach. Within the framework of HMGRL, we leverage a wealth of drug-related ...

Subgraph-conditioned Graph Information Bottleneck (S-CGIB) is a novel architecture for pre-training Graph Neural Networks in molecular property prediction and developed by NS Lab, CUK based on pure ...

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 ...

These methods ignore a characteristic of gang operation in money laundering. Thus, in this paper, we propose a multi-view graph based hierarchical representation learning method, named MG-HRL, to mine ...

Data shows that AI Overviews is answering more complex queries by up to 100% and is rewarding context appropriate content ...

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 ...

How big of a problem is it worldwide? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature.

Discover how individual neurons in the brain learn to recognize and predict patterns in the flow of time, providing insights into the structure of time.