We present the analysis of the topological graph descriptor Local Degree
Profile (LDP), which forms a widely used structural baseline for graph
classification. Our study focuses on model evaluation in the context of the
recently developed fair evaluation framework, which defines rigorous routines
for model selection and evaluation for graph classification, ensuring
reproducibility and comparability of the results. Based on the obtained
insights, we propose a new baseline algorithm called Local Topological Profile
(LTP), which extends LDP by using additional centrality measures and local
vertex descriptors. The new approach provides the results outperforming or very
close to the latest GNNs for all datasets used. Specifically, state-of-the-art
results were obtained for 4 out of 9 benchmark datasets. We also consider
computational aspects of LDP-based feature extraction and model construction to
propose practical improvements affecting execution speed and scalability. This
allows for handling modern, large datasets and extends the portfolio of
benchmarks used in graph representation learning. As the outcome of our work,
we obtained LTP as a simple to understand, fast and scalable, still robust
baseline, capable of outcompeting modern graph classification models such as
Graph Isomorphism Network (GIN). We provide open-source implementation at
\href{this https URL}{GitHub}.

本文通过使用公平评估框架，对局部度量描述符 (Local Degree Profile) 进行分析，并提出了基于局部中心性量度和局部顶点描述符扩展的新算法 (Local Topological Profile)，其结果在大多数数据集上优于或接近于现代图分类模型 Graph Isomorphism Network (GIN)。而且，研究了 LDP 特征提取和模型构建的计算方面，提出了可行的高效实现方案，并将其开源。