# COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

arXiv: https://arxiv.org/abs/1507.02293 · HTML: https://mlsanity.com/paper/1507.02293/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2015.

## Built on by (most cited first, 30 of 37)

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- [On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data](https://mlsanity.com/paper/2306.17066/) (2023)
- [Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes](https://mlsanity.com/paper/2105.11152/) (2021)
- [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://mlsanity.com/paper/2401.04612/) (2024)
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- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
- [Byte-token Enhanced Language Models for Temporal Point Processes Analysis](https://mlsanity.com/paper/2502.07139/) (2025)
- [The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks](https://mlsanity.com/paper/2205.00639/) (2022)
- [Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models](https://mlsanity.com/paper/2201.05044/) (2022)
- [Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels](https://mlsanity.com/paper/2104.01029/) (2021)
- [Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning](https://mlsanity.com/paper/2404.13663/) (2024)
- [PCA for Point Processes](https://mlsanity.com/paper/2404.19661/) (2024)
- [On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes](https://mlsanity.com/paper/2406.00630/) (2024)
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- [Learning Neural Models for Continuous-Time Sequences](https://mlsanity.com/paper/2111.07189/) (2021)
- [SimHawNet: A Modified Hawkes Process for Temporal Network Simulation](https://mlsanity.com/paper/2203.07260/) (2022)
- [Differentially Private Learning of Hawkes Processes](https://mlsanity.com/paper/2207.13741/) (2022)
- [A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior](https://mlsanity.com/paper/2411.04397/) (2024)
- [Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes](https://mlsanity.com/paper/2602.04798/) (2026)
- [Hawkes Processes on Graphons](https://mlsanity.com/paper/2102.02741/) (2021)
- [Latent Network Embedding via Adversarial Auto-encoders](https://mlsanity.com/paper/2109.15257/) (2021)
- [How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process](https://mlsanity.com/paper/2112.03008/) (2021)
- [CEP3: Community Event Prediction with Neural Point Process on Graph](https://mlsanity.com/paper/2205.10624/) (2022)
- [Dynamic Healthcare Embeddings for Improving Patient Care](https://mlsanity.com/paper/2303.11563/) (2023)
- [Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes](https://mlsanity.com/paper/2305.18412/) (2023)

## Abstract (arXiv)

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.