← Back to all news
arXiv Machine Learning By Bryant Pollard

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

Read the original on arXiv Machine Learning →

EdgeReMIND is a linear memorization model designed for temporal multi-relational link prediction on the Temporal Graph Benchmark 2.0 (TGB 2.0). It achieves the highest reported test mean reciprocal rank (MRR) on six of eight TGB 2.0 datasets and is the only relation‑aware method that runs on all datasets, overcoming the scalability ceiling that limits existing embedding methods. The model uses learned per‑relation weights over data‑calibrated features, making it a practical state‑of‑the‑art baseline for large‑scale temporal graphs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

Hugging Face Trending Papers
5d ago

Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters

The paper investigates whether temporal link predictors can forgo learned node representations in favor of simple statistical counts of interaction patterns. It introduces a predictor that aggregates transition and co‑occurrence counts, smooths them with destination frequencies or Kneser‑Ney continuation counts, and combines these with popularity, source history, and recency via a shared log‑linear rule. With only 9–13 learned parameters, the model achieves the best mean reciprocal rank on 7 of 16 datasets and outperforms several baselines across all evaluated datasets, demonstrating that a lightweight, count‑based approach can rival more complex neural methods.

rag
More like this →
arXiv AI
Sep 10

TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs

TTGBench is a new benchmark for temporal graph learning that evaluates both structural evolution and semantic drift in text‑attributed graphs. It includes six real‑world, text‑rich datasets with dual volatility and supports multi‑class and multi‑label temporal node classification, addressing gaps left by existing benchmarks. A comprehensive evaluation of 17 state‑of‑the‑art methods shows a clear divide: TGNNs excel at structural prediction but struggle with semantic tracking, while LLM‑based models perform better on semantic tasks but lag in structural prediction.

By Longfei Ma, Zemin Liu, Fei Wu
llmsbenchmarks
More like this →
arXiv Machine Learning
Jun 5

The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning

arXiv:2606. 06397v1 Announce Type: new Abstract: Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure.

By Shuo Wang, Xiangyu Wang, Quanxin Wang, Bailin Wu, Bokui Wang, Shunyang Huang, Boyan Deng, Haonan Liu, Ruiyi Fang, Zhenxiang Xu, Boyu Wang, Zhao Kang
benchmarkssafety
More like this →