IEEE

Multi-Level Task-Agnostic Graph Representation Learning With Isomorphic-Consistent Variational Graph Auto-Encoders

Abstract: Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice ...
The Manila Times

DataGlobal Hub’s GDAI 2025 Showcases the World’s Brightest Minds in Data and AI

Global Data & AI Virtual Tech Conference (GDAI 2025), the biggest virtual tech conference organized by DataGlobal Hub, was concluded with resounding success, uniting participants from various ...
IEEE

Context-Aware Graph Inference and Generative Adversarial Imitation Learning for Object-Goal Navigation in Unfamiliar Environment

Abstract: Object-goal navigation aims to guide an agent to find a specific target object in an unfamiliar environment based on first-person visual observations. It requires the agent to learn ...
GitHub

Implementation of "BiGDN: An End-To-End Influence Maximization Framework Based on Deep Reinforcement Learning and Graph Neural Networks"

python test.py --test_graph_path test_data/Wiki-2.txt \ --teacher\ --model q_net.pth \ --k 10 --selection_strategy o test_graphs: there are 8 test graphs, the YouTube graph file is too large to upload ...
ZDNet

Graph databases are exploding, thanks to the AI boom - here's why

The graph database market, driven by AI, is growing at a rate of almost 25% annually. Graph databases support knowledge graphs, providing visual guidance for AI development. There are multiple ...
GitHub

AWS MCP Servers

What is the Model Context Protocol (MCP) and how does it work with AWS MCP Servers? The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and ...