# A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks

arXiv: https://arxiv.org/abs/2209.07332 · HTML: https://mlsanity.com/paper/2209.07332/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [On Valid Optimal Assignment Kernels and Applications to Graph Classification](https://mlsanity.com/paper/1606.01141/) (2016)
- [The Multiscale Laplacian Graph Kernel](https://mlsanity.com/paper/1603.06186/) (2016)
- [Modern temporal network theory: A colloquium](https://mlsanity.com/paper/1508.01303/) (2015)
- [Temporal Graph Kernels for Classifying Dissemination Processes](https://mlsanity.com/paper/1911.05496/) (2019)
- [Wedge Sampling for Computing Clustering Coefficients and Triangle Counts on Large Graphs](https://mlsanity.com/paper/1309.3321/) (2013)
- [Temporal Network Motifs: Models, Limitations, Evaluation](https://mlsanity.com/paper/2005.11817/) (2020)
- [A Chronological Edge-Driven Approach to Temporal Subgraph Isomorphism](https://mlsanity.com/paper/1801.08098/) (2018)
- [Dynamic Graph Echo State Networks](https://mlsanity.com/paper/2110.08565/) (2021)
- [A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels](https://mlsanity.com/paper/1703.00676/) (2017)
- [Detecting sequences of system states in temporal networks](https://mlsanity.com/paper/1803.04755/) (2018)
- [Motif Discovery Algorithms in Static and Temporal Networks: A Survey](https://mlsanity.com/paper/2005.09721/) (2020)
- [odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks](https://mlsanity.com/paper/2108.08734/) (2021)
- [Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces](https://mlsanity.com/paper/1704.06498/) (2017)

## Abstract (arXiv)

We introduce the temporal graphlet kernel for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks are modeled as labeled temporal graphs, in which the edges exist at specific points in time, and node labels change over time. The classification problem asks to discriminate dissemination processes of different origins or parameters, e.g., infectious diseases with different infection probabilities. Our new kernel represents labeled temporal graphs in the feature space of temporal graphlets, i.e., small subgraphs distinguished by their structure, time-dependent node labels, and chronological order of edges. We introduce variants of our kernel based on classes of graphlets that are efficiently countable. For the case of temporal wedges, we propose a highly efficient approximative kernel with low error in expectation. We show that our kernels are faster to compute and provide better accuracy than state-of-the-art methods.