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Graphs are ubiquitous in modeling relational data, but static graphs fail to capture the evolving nature of real-world systems. While models like TGN, JODIE, and TGAT have advanced the field, the tooling supporting them remains archaic. Current workflows rely on disjointed scripts for downloading datasets (e.g., Reddit, Wikipedia, MOOC), splitting temporal edges, and constructing negative samples. tgxgoodies best

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The library is built on three core pillars, designed to integrate seamlessly with PyTorch and PyG (PyTorch Geometric): From the latest Hollywood blockbusters to popular streaming

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Despite its reputation, TGxGoodies has faced significant challenges, throwing its future into uncertainty. Understanding these issues is crucial for any user who relies on its uploads. While models like TGN, JODIE, and TGAT have

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We have presented tgxgoodies , a library that sets a new benchmark for utility in temporal graph learning. Our evaluation confirms that tgxgoodies offers the best combination of speed, efficiency, and standardization. We release the library under an MIT license to encourage community-driven improvements.

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