A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks

TL;DR

This paper presents a novel temporal graphlet kernel for classifying dissemination processes in evolving networks, capturing structural, temporal, and label information to improve accuracy and efficiency over existing methods.

Contribution

The paper introduces a new temporal graphlet kernel that effectively models dynamic networks for dissemination classification, with variants for efficient computation and improved accuracy.

Findings

Kernel outperforms state-of-the-art methods in accuracy.

Proposed variants enable efficient computation on large graphs.

Temporal wedge kernel achieves low error with high efficiency.

Abstract

We introduce the \emph{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.