PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts

TL;DR

This paper introduces PRESTO, a scalable sampling algorithm for accurately approximating temporal motif counts in large networks, outperforming existing methods in speed and accuracy.

Contribution

The authors propose a simple, effective sampling technique that provides rigorous approximations of temporal motif counts, enabling analysis of very large datasets.

Findings

More accurate estimates than state-of-the-art sampling algorithms

Significantly faster than exact counting methods

Capable of analyzing networks with over a billion edges

Abstract

The identification and counting of small graph patterns, called network motifs, is a fundamental primitive in the analysis of networks, with application in various domains, from social networks to neuroscience. Several techniques have been designed to count the occurrences of motifs in static networks, with recent work focusing on the computational challenges provided by large networks. Modern networked datasets contain rich information, such as the time at which the events modeled by the networks edges happened, which can provide useful insights into the process modeled by the network. The analysis of motifs in temporal networks, called temporal motifs, is becoming an important component in the analysis of modern networked datasets. Several methods have been recently designed to count the number of instances of temporal motifs in temporal networks, which is even more challenging than its counterpart for static networks. Such methods are either exact, and not applicable to large networks, or approximate, but provide only weak guarantees on the estimates they produce and do not scale to very large networks. In this work we present an efficient and scalable algorithm to obtain rigorous approximations of the count of temporal motifs. Our algorithm is based on a simple but effective sampling approach, which renders our algorithm practical for very large datasets. Our extensive experimental evaluation shows that our algorithm provides estimates of temporal motif counts which are more accurate than the state-of-the-art sampling algorithms, with significantly lower running time than exact approaches, enabling the study of temporal motifs, of size larger than the ones considered in previous works, on billion edges networks.