# Temporal Graph Kernels for Classifying Dissemination Processes

arXiv: https://arxiv.org/abs/1911.05496 · HTML: https://mlsanity.com/paper/1911.05496/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data](https://mlsanity.com/paper/2107.02168/) (2021)
- [A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening](https://mlsanity.com/paper/2306.08854/) (2023)
- [Graphon based Clustering and Testing of Networks: Algorithms and Theory](https://mlsanity.com/paper/2110.02722/) (2021)
- [Dynamic Graph Echo State Networks](https://mlsanity.com/paper/2110.08565/) (2021)
- [Interpreting Temporal Graph Neural Networks with Koopman Theory](https://mlsanity.com/paper/2410.13469/) (2024)
- [Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs](https://mlsanity.com/paper/2505.24438/) (2025)
- [Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks](https://mlsanity.com/paper/2306.03434/) (2023)
- [Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification](https://mlsanity.com/paper/2307.01237/) (2023)
- [Nested stochastic block model for simultaneously clustering networks and nodes](https://mlsanity.com/paper/2307.09210/) (2023)
- [Filtration Surfaces for Dynamic Graph Classification](https://mlsanity.com/paper/2309.03616/) (2023)
- [Relevant Walk Search for Explaining Graph Neural Networks](https://mlsanity.com/paper/2605.23673/) (2026)
- [LAGOON: An Analysis Tool for Open Source Communities](https://mlsanity.com/paper/2201.11657/) (2022)
- [A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks](https://mlsanity.com/paper/2209.07332/) (2022)
- [Filtration-Based Representation Learning for Temporal Graphs](https://mlsanity.com/paper/2502.10076/) (2025)

## Abstract (arXiv)

Many real-world graphs or networks are temporal, e.g., in a social network persons only interact at specific points in time. This information directs dissemination processes on the network, such as the spread of rumors, fake news, or diseases. However, the current state-of-the-art methods for supervised graph classification are designed mainly for static graphs and may not be able to capture temporal information. Hence, they are not powerful enough to distinguish between graphs modeling different dissemination processes. To address this, we introduce a framework to lift standard graph kernels to the temporal domain. Specifically, we explore three different approaches and investigate the trade-offs between loss of temporal information and efficiency. Moreover, to handle large-scale graphs, we propose stochastic variants of our kernels with provable approximation guarantees. We evaluate our methods on a wide range of real-world social networks. Our methods beat static kernels by a large margin in terms of accuracy while still being scalable to large graphs and data sets. Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.