CITE: A Comprehensive Benchmark for Heterogeneous Text-Attributed Graphs on Catalytic Materials

TL;DR

This paper introduces CITE, the first large-scale heterogeneous text-attributed graph benchmark for catalytic materials, enabling fair comparison and development of graph representation learning methods.

Contribution

It provides a comprehensive, large-scale dataset and standardized evaluation procedures for heterogeneous TAGs in catalytic materials research.

Findings

Benchmark dataset with 438K nodes and 1.2M edges.

Comparison of four learning paradigms on node classification.

Extensive ablation studies on heterogeneity and textual features.

Abstract

Text-attributed graphs(TAGs) are pervasive in real-world systems,where each node carries its own textual features. In many cases these graphs are inherently heterogeneous, containing multiple node types and diverse edge types. Despite the ubiquity of such heterogeneous TAGs, there remains a lack of large-scale benchmark datasets. This shortage has become a critical bottleneck, hindering the development and fair comparison of representation learning methods on heterogeneous text-attributed graphs. In this paper, we introduce CITE - Catalytic Information Textual Entities Graph, the first and largest heterogeneous text-attributed citation graph benchmark for catalytic materials. CITE comprises over 438K nodes and 1.2M edges, spanning four relation types. In addition, we establish standardized evaluation procedures and conduct extensive benchmarking on the node classification task, as well as ablation experiments on the heterogeneous and textual properties of CITE. We compare four classes of learning paradigms, including homogeneous graph models, heterogeneous graph models, LLM(Large Language Model)-centric models, and LLM+Graph models. In a nutshell, we provide (i) an overview of the CITE dataset, (ii) standardized evaluation protocols, and (iii) baseline and ablation experiments across diverse modeling paradigms.