Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link
Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to …

This paper introduces a conceptually simple, scalable, and highly effective BERT-based
entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We …

In this work, we move beyond the traditional complex-valued representations, introducing
more expressive hypercomplex representations to model entities and relations for …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Inspired by the great success of neural networks, graph convolutional neural networks
(GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct …

Pre-trained language models (PLMs) are fundamental for natural language processing
applications. Most existing PLMs are not tailored to the noisy user-generated text on social …