In today's data-rich environment, business are always looking for a way to capitalize on available data for new insights and ...
Abstract: Transformers have gained prominence in natural language processing due to their representational capabilities and performances. Transformers process natural language as a sequence on finite ...
STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
The Competition Commission has published its inaugural Cost of Living Report, which assesses the affordability of basic goods and services in South Africa. The report stems from the commission’s ...
Abstract: Heterogeneous graph neural networks (HGNNs) have proven effective at capturing complex relationships in graphs with diverse node and edge types. However, centralized training in HGNNs raises ...
To develop the tutorial code, I referred to the repository of the MARS dataset used in the paper: [GitHub] First, as instructed in the README of the MARS repository ...
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 ...
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