# Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs

arXiv: https://arxiv.org/abs/2310.15865 · HTML: https://mlsanity.com/paper/2310.15865/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Temporal Graph Benchmark for Machine Learning on Temporal Graphs](https://mlsanity.com/paper/2307.01026/) (2023)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Modern temporal network theory: A colloquium](https://mlsanity.com/paper/1508.01303/) (2015)
- [Memory in network flows and its effects on spreading dynamics and community detection](https://mlsanity.com/paper/1305.4807/) (2013)
- [Can co-location be used as a proxy for face-to-face contacts?](https://mlsanity.com/paper/1712.06346/) (2017)
- [Causality-Driven Slow-Down and Speed-Up of Diffusion in Non-Markovian Temporal Networks](https://mlsanity.com/paper/1307.4030/) (2013)
- [When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks](https://mlsanity.com/paper/1702.05499/) (2017)
- [De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs](https://mlsanity.com/paper/2209.08311/) (2022)
- [Machine Learning in Network Centrality Measures: Tutorial and Outlook](https://mlsanity.com/paper/1810.11760/) (2018)
- [Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path structures and centralities](https://mlsanity.com/paper/1508.06467/) (2015)
- [Approximating Network Centrality Measures Using Node Embedding and Machine Learning](https://mlsanity.com/paper/2006.16392/) (2020)
- [ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages](https://mlsanity.com/paper/1602.05866/) (2016)
- [Using higher-order Markov models to reveal flow-based communities in networks](https://mlsanity.com/paper/1601.03516/) (2016)
- [Algorithmic Aspects of Temporal Betweenness](https://mlsanity.com/paper/2006.08668/) (2020)
- [ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks](https://mlsanity.com/paper/2203.00653/) (2022)
- [Approximating Betweenness Centrality in Large Evolving Networks](https://mlsanity.com/paper/1409.6241/) (2014)
- [Counting Causal Paths in Big Times Series Data on Networks](https://mlsanity.com/paper/1905.11287/) (2019)
- [Efficient limited-time reachability estimation in temporal networks](https://mlsanity.com/paper/1908.11831/) (2019)
- [Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures](https://mlsanity.com/paper/1906.09576/) (2019)

## Built on by (most cited first, 5 of 5)

- [Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs](https://mlsanity.com/paper/2505.24438/) (2025)
- [Deep Graph Learning will stall without Network Science](https://mlsanity.com/paper/2502.01177/) (2025)
- [Degree-Mass Message Passing for Betweenness Ranking in Directed and Undirected Networks](https://mlsanity.com/paper/2602.09716/) (2026)
- [CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs](https://mlsanity.com/paper/2506.14122/) (2025)
- [Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs](https://mlsanity.com/paper/2610.02990/) (2026)

## Abstract (arXiv)

Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betweenness can give misleading results about the true importance of nodes in a temporal graph. To address this issue, temporal generalizations of betweenness and closeness have been defined that are based on the shortest time-respecting paths between pairs of nodes. However, a major issue of those generalizations is that the calculation of such paths is computationally expensive. Addressing this issue, we study the application of De Bruijn Graph Neural Networks (DBGNN), a time-aware graph neural network architecture, to predict temporal path-based centralities in time series data. We experimentally evaluate our approach in 13 temporal graphs from biological and social systems and show that it considerably improves the prediction of betweenness and closeness centrality compared to (i) a static Graph Convolutional Neural Network, (ii) an efficient sampling-based approximation technique for temporal betweenness, and (iii) two state-of-the-art time-aware graph learning techniques for dynamic graphs.