Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

TL;DR

This paper introduces a scalable, efficient framework for detecting recurring activity patterns in graphs, applicable to diverse real-world scenarios like social networks, pedestrian congestion, and music recommendation systems.

Contribution

The authors propose a novel multilayer graph approach for analyzing dynamic processes on graphs, demonstrating its effectiveness across multiple domains with high scalability.

Findings

Successfully applied to social, pedestrian, and audio data

Handles millions of nodes with linear scalability

Extracts hidden patterns efficiently

Abstract

Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framework for retrieving and analyzing recurring patterns of activity on graphs. Our method relies on a novel type of multilayer graph that encodes the spreading or propagation of events between successive time steps. We demonstrate the versatility of our method by applying it on three different real-world examples. Firstly, we study how rumor spreads on a social network. Secondly, we reveal congestion patterns of pedestrians in a train station. Finally, we show how patterns of audio playlists can be used in a recommender system. In each example, relevant information previously hidden in the data is extracted in a very efficient manner, emphasizing the scalability of our method. With a parallel implementation scaling linearly with the size of the dataset, our framework easily handles millions of nodes on a single commodity server.