Papers › Subgraph Networks with Application to Structural Feature Space Expansion
Subgraph Networks with Application to Structural Feature Space Expansion
Qi Xuan, Jinhuan Wang, Minghao Zhao, Junkun Yuan, Chenbo Fu, Zhongyuan Ruan, Guanrong Chen
Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | IMDb-B | Deep WL SGN(0,1,2) | Accuracy | 75.70% | #20 of 51 | Archive leaderboard | report |
| Graph Classification | MUTAG | Deep WL SGN(0,1,2) | Accuracy | 93.68% | #10 of 74 | Archive leaderboard | report |
| Graph Classification | NCI1 | Deep WL SGN(0,1,2) | Accuracy | 70.26% | #62 of 69 | Archive leaderboard | report |
| Graph Classification | NCI109 | Deep WL SGN(0,1,2) | Accuracy | 71.06 | #34 of 38 | Archive leaderboard | report |
| Graph Classification | PROTEINS | Deep WL SGN(0,1,2) | Accuracy | 76.78% | #40 of 103 | Archive leaderboard | report |
| Graph Classification | PTC | Deep WL SGN(0,1,2) | Accuracy | 65.88% | #21 of 37 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections