# Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces

## TL;DR

This paper introduces a novel approach for predicting the future state of entire graphs over time using kernel and dissimilarity-based regression techniques, enabling better understanding of graph dynamics in various fields.

## Contribution

It proposes framing graph evolution prediction as a regression problem with kernel and dissimilarity methods, including strategies to accelerate Gaussian process regression.

## Key findings

- Kernel regression captures theoretical graph dynamics effectively.
- Gaussian process regression improves accuracy on real-world data.
- Speed-up strategies make Gaussian processes computationally feasible.

## Abstract

Graph models are relevant in many fields, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in the graph structure into account, i.e. a varying number of nodes or edges. Predicting such changes within graphs can be expected to yield important insight with respect to the underlying dynamics, e.g. with respect to user behaviour. However, predictive techniques in the past have almost exclusively focused on single edges or nodes. In this contribution, we attempt to predict the future state of a graph as a whole. We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels. The output of the regression is a point embedded in a pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity- or kernel-based processing methods. We discuss strategies to speed up Gaussian Processes regression from cubic to linear time and evaluate our approach on two well-established theoretical models of graph evolution as well as two real data sets from the domain of intelligent tutoring systems. We find that simple regression methods, such as kernel regression, are sufficient to capture the dynamics in the theoretical models, but that Gaussian process regression significantly improves the prediction error for real-world data.