Hongyu Ren

TL;DR: The OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs, indicating fruitful opportunities for future research.

TL;DR: The Holistic Evaluation of Language Models (HELM) as mentioned in this paper ) is a popular benchmark for language models, with 30 models evaluated on 16 core scenarios and 7 metrics, exposing important trade-offs.

TL;DR: Query2box as discussed by the authors is an embedding-based framework for reasoning over complex logical queries on large-scale incomplete knowledge graphs, where queries can be embedded as boxes (i.e., hyper-rectangles).

TL;DR: BetaE is the first method that can handle a complete set of first-order logical operations: conjunction, disjunction, and negation, and a key insight of BetaE is to use probabilistic distributions with bounded support, specifically the Beta distribution, and embed queries/entities as distributions, which as a consequence allows us to also faithfully model uncertainty.

TL;DR: Open Graph Benchmark (OGB) as discussed by the authors is a set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research, covering a diverse range of domains including social and information networks, biological networks, molecular graphs, source code ASTs, and knowledge graphs.