# The Temporal Event Graph

arXiv: https://arxiv.org/abs/1706.02128 · HTML: https://mlsanity.com/paper/1706.02128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces](https://mlsanity.com/paper/2511.07587/) (2025)
- [Creating an AI Observer: Generative Semantic Workspaces](https://mlsanity.com/paper/2406.04555/) (2024)

## Abstract (arXiv)

Temporal networks are increasingly being used to model the interactions of complex systems. Most studies require the temporal aggregation of edges (or events) into discrete time steps to perform analysis. In this article we describe a static, lossless, and unique representation of a temporal network, the temporal event graph (TEG). The TEG describes the temporal network in terms of both the inter-event time and two-event temporal motif distributions. By considering these distributions in unison we provide a new method to characterise the behaviour of individuals and collectives in temporal networks as well as providing a natural decomposition of the network. We illustrate the utility of the TEG by providing examples on both synthetic and real temporal networks.