# Subgraph Networks with Application to Structural Feature Space Expansion

arXiv: https://arxiv.org/abs/1903.09022 · HTML: https://mlsanity.com/paper/1903.09022/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 16 of 16)

- [SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism](https://mlsanity.com/paper/2101.08170/) (2021)
- [Identity Inference on Blockchain using Graph Neural Network](https://mlsanity.com/paper/2104.06559/) (2021)
- [Adaptive Visibility Graph Neural Network and its Application in Modulation Classification](https://mlsanity.com/paper/2106.08564/) (2021)
- [TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts](https://mlsanity.com/paper/2104.08767/) (2021)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking](https://mlsanity.com/paper/2102.08013/) (2021)
- [Supervised Contrastive Learning with Structure Inference for Graph Classification](https://mlsanity.com/paper/2203.07691/) (2022)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [CLPVG: Circular limited penetrable visibility graph as a new network model for time series](https://mlsanity.com/paper/2104.13772/) (2021)
- [Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG](https://mlsanity.com/paper/2603.07179/) (2026)
- [Revisiting 2D Convolutional Neural Networks for Graph-based Applications](https://mlsanity.com/paper/2105.11016/) (2021)
- [Motif-driven Subgraph Structure Learning for Graph Classification](https://mlsanity.com/paper/2406.08897/) (2024)
- [SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph](https://mlsanity.com/paper/2205.03557/) (2022)
- [Subgraph Networks Based Contrastive Learning](https://mlsanity.com/paper/2306.03506/) (2023)
- [A Structural Feature-Based Approach for Comprehensive Graph Classification](https://mlsanity.com/paper/2408.05474/) (2024)
- [Adaptive Substructure-Aware Expert Model for Molecular Property Prediction](https://mlsanity.com/paper/2504.05844/) (2025)

## Abstract (arXiv)

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.