A FAIR COMPARISON OF GRAPH NEURAL NETWORKS FOR GRAPH CLASSIFICATION

A Fair Comparison of Graph Neural Networks for Graph Classification

Federico Errica, Macro Podda, Davide Bacciu, Alessio Micheli

Intro

Evaluate 5 models across 9 benchmarks.

1. Provide a fair comparison among GNN architectures. Perform a large number of experiments.
2. Explore to what extent current GNN models can effectively exploit graph structure.
3. Study the effect of node degrees as a feature in social datasets. Including degrees is beneficial to the performance, and it also has implications in the number of GNN layers needed to reach good results.

Experiments

Baselines 用了两个不同的baselines： chemical 上用的是Molecular Fingerprint。 social 上用的是MLP （node features） + global sum pooling + MLP。 并不包含/利用graph topology。

因此是structure-agnostic：将structure info和node info很好的分离开来。 而且能够发现一旦这个baseline和GNN的差别不大，那就表示GNN not exploiting the structure for graph adequately。 而GNN明显比baselines好的时候，表示topology被很好的利用了。

Results and Discussion

• Table 3:只有一个数据集上GNN比（且都比）baselines好，其余都是baselines更优。
• Table 4:增加node degrees能明显提高performance。