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EdgeReMIND model sets new benchmark for temporal link prediction

By PulseAugur Editorial · [1 sources] ·

A new paper introduces EdgeReMIND, a scalable memorization baseline designed for temporal multi-relational link prediction. This model addresses the scalability limitations of existing embedding methods on large-scale temporal graphs, which are crucial for real-world applications. EdgeReMIND achieves top-ranked performance on the Temporal Graph Benchmark 2.0, outperforming other relation-aware methods and demonstrating its practicality as a state-of-the-art baseline. AI

IMPACT Provides a more scalable and effective baseline for temporal link prediction tasks, crucial for real-world graph data analysis.

RANK_REASON The cluster contains a research paper detailing a new model and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EdgeReMIND model sets new benchmark for temporal link prediction

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The cluster contains a research paper detailing a new model and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bryant Pollard ·

    EdgeReMIND: A Scalable, Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Prediction

    arXiv:2609.17916v1 Announce Type: new Abstract: Temporal link prediction on the Temporal Graph Benchmark 2.0 (TGB 2.0) faces a scalability ceiling: on the benchmark's three largest datasets, every existing embedding method runs out of memory or exceeds the time budget. These larg…

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