Overview Structured Python learning path that moves from fundamentals (syntax, loops, functions) to real data science tools ...

Abstract: Dynamic graph neural networks (DGNNs) have emerged as a leading paradigm for learning from dynamic graphs, which are commonly used to model real-world systems and applications. However, due ...

Abstract: Dynamic link prediction in temporal networks is crucial for applications in the areas such as social network analysis, recommender systems, and financial risk forecasting. Although existing ...