# Modern temporal network theory: A colloquium

arXiv: https://arxiv.org/abs/1508.01303 · HTML: https://mlsanity.com/paper/1508.01303/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2015.

## Built on by (most cited first, 23 of 23)

- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://mlsanity.com/paper/2304.05729/) (2023)
- [Disentangled Spatiotemporal Graph Generative Models](https://mlsanity.com/paper/2203.00411/) (2022)
- [DYMOND: DYnamic MOtif-NoDes Network Generative Model](https://mlsanity.com/paper/2308.00770/) (2023)
- [On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions](https://mlsanity.com/paper/2103.07016/) (2021)
- [A Survey of Link Prediction in Temporal Networks](https://mlsanity.com/paper/2502.21185/) (2025)
- [Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs](https://mlsanity.com/paper/2310.15865/) (2023)
- [A Survey on Temporal Graph Representation Learning and Generative Modeling](https://mlsanity.com/paper/2208.12126/) (2022)
- [PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts](https://mlsanity.com/paper/2101.07152/) (2021)
- [Investigating and Modeling the Dynamics of Long Ties](https://mlsanity.com/paper/2109.10523/) (2021)
- [Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs](https://mlsanity.com/paper/2505.24438/) (2025)
- [Robust Dynamic Network Embedding via Ensembles](https://mlsanity.com/paper/2105.14557/) (2021)
- [Leveraging the Graph Structure of Neural Network Training Dynamics](https://mlsanity.com/paper/2111.05410/) (2021)
- [Deep Graph Learning will stall without Network Science](https://mlsanity.com/paper/2502.01177/) (2025)
- [Community Detection in Weighted Multilayer Networks with Ambient Noise](https://mlsanity.com/paper/2103.00486/) (2021)
- [Frequent Pattern Mining in Continuous-time Temporal Networks](https://mlsanity.com/paper/2105.06399/) (2021)
- [Recovering lost and absent information in temporal networks](https://mlsanity.com/paper/2107.10835/) (2021)
- [Exploring and mining attributed sequences of interactions](https://mlsanity.com/paper/2107.13329/) (2021)
- [Inference of time-ordered multibody interactions](https://mlsanity.com/paper/2111.14611/) (2021)
- [Minorities in networks and algorithms](https://mlsanity.com/paper/2206.07113/) (2022)
- [A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks](https://mlsanity.com/paper/2209.07332/) (2022)
- [Time-Varying Data as Sheaves: an Invitation to Narratives](https://mlsanity.com/paper/2609.09056/) (2026)

## Abstract (arXiv)

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.