# Network Classification in Temporal Networks Using Motifs

arXiv: https://arxiv.org/abs/1807.03733 · HTML: https://mlsanity.com/paper/1807.03733/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models](https://mlsanity.com/paper/2311.09862/) (2023)
- [PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts](https://mlsanity.com/paper/2101.07152/) (2021)
- [Filtration-Based Representation Learning for Temporal Graphs](https://mlsanity.com/paper/2502.10076/) (2025)

## Abstract (arXiv)

Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accuracy by up 10% compared to the state-of-the-art embedding method in network classification, for tasks such as classifying network type, identifying communities in email exchange network, and identifying users given their app-switching behaviors.