Papers › Graph Classification with 2D Convolutional Neural Networks
Graph Classification with 2D Convolutional Neural Networks
Antoine Jean-Pierre Tixier, Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced. In this paper, we reverse the problem: rather than proposing yet another graph CNN model, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs. Experiments reveal that our method is more accurate than state-of-the-art graph kernels and graph CNNs on 4 out of 6 real-world datasets (with and without continuous node attributes), and close elsewhere. Our approach is also preferable to graph kernels in terms of time complexity. Code and data are publicly available.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | COLLAB | 2D CNN | Accuracy | 71.76% | #30 of 39 | Archive leaderboard | report |
| Graph Classification | IMDb-B | 2D CNN | Accuracy | 70.40% | #45 of 51 | Archive leaderboard | report |
| Graph Classification | RE-M12K | 2D CNN | Accuracy | 48.13% | #3 of 6 | Archive leaderboard | report |
| Graph Classification | RE-M5K | 2D CNN | Accuracy | 52.11% | #5 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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