# A Chronological Edge-Driven Approach to Temporal Subgraph Isomorphism

arXiv: https://arxiv.org/abs/1801.08098 · HTML: https://mlsanity.com/paper/1801.08098/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts](https://mlsanity.com/paper/2101.07152/) (2021)
- [odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks](https://mlsanity.com/paper/2108.08734/) (2021)
- [Scalable Motif Counting for Large-scale Temporal Graphs](https://mlsanity.com/paper/2204.09236/) (2022)
- [A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks](https://mlsanity.com/paper/2209.07332/) (2022)
- [Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs](https://mlsanity.com/paper/2504.15979/) (2025)
- [Efficient Approximate Temporal Triangle Counting in Streaming with Predictions](https://mlsanity.com/paper/2506.13173/) (2025)

## Abstract (arXiv)

Many real world networks are considered temporal networks, in which the chronological ordering of the edges has importance to the meaning of the data. Performing temporal subgraph matching on such graphs requires the edges in the subgraphs to match the order of the temporal graph motif we are searching for. Previous methods for solving this rely on the use of static subgraph matching to find potential matches first, before filtering them based on edge order to find the true temporal matches. We present a new algorithm for temporal subgraph isomorphism that performs the subgraph matching directly on the chronologically sorted edges. By restricting our search to only the subgraphs with chronologically correct edges, we can improve the performance of the algorithm significantly. We present experimental timing results to show significant performance improvements on publicly available datasets for a number of different temporal query graph motifs with four or more nodes. We also demonstrate a practical example of how temporal subgraph isomorphism can produce more meaningful results than traditional static subgraph searches.