# Temporal Network Motifs: Models, Limitations, Evaluation

arXiv: https://arxiv.org/abs/2005.11817 · HTML: https://mlsanity.com/paper/2005.11817/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 9 of 9)

- [TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery](https://mlsanity.com/paper/2310.19324/) (2023)
- [Using Motif Transitions for Temporal Graph Generation](https://mlsanity.com/paper/2306.11190/) (2023)
- [LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs](https://mlsanity.com/paper/2512.22266/) (2025)
- [PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts](https://mlsanity.com/paper/2101.07152/) (2021)
- [Scalable Motif Counting for Large-scale Temporal Graphs](https://mlsanity.com/paper/2204.09236/) (2022)
- [Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms](https://mlsanity.com/paper/2301.07791/) (2023)
- [A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks](https://mlsanity.com/paper/2209.07332/) (2022)
- [MotifDisco: Motif Causal Discovery For Time Series Motifs](https://mlsanity.com/paper/2409.15219/) (2024)
- [Efficient Approximate Temporal Triangle Counting in Streaming with Predictions](https://mlsanity.com/paper/2506.13173/) (2025)

## Abstract (arXiv)

Investigating the frequency and distribution of small subgraphs with a few nodes/edges, i.e., motifs, is an effective analysis method for static networks. Motif-driven analysis is also useful for temporal networks where the spectrum of motifs is significantly larger due to the additional temporal information on edges. This variety makes it challenging to design a temporal motif model that can consider all aspects of temporality. In the literature, previous works have introduced various models that handle different characteristics. In this work, we compare the existing temporal motif models and evaluate the facets of temporal networks that are overlooked in the literature. We first survey four temporal motif models and highlight their differences. Then, we evaluate the advantages and limitations of these models with respect to the temporal inducedness and timing constraints. In addition, we suggest a new lens, event pairs, to investigate temporal correlations. We believe that our comparative survey and extensive evaluation will catalyze the research on temporal network motif models.