Understanding Information Flow in Cascades Using Network Motifs

TL;DR

This paper investigates the use of network motifs to understand the structure of information cascades over time, revealing specific patterns associated with different cascade phases, especially the inhibition phase.

Contribution

It introduces a motif percolation-based algorithm to analyze cascade network organization and compares motif patterns during different cascade phases.

Findings

Few specific triad motifs characterize the inhibition phase.

Motifs are less indicative during the steep growth phase.

Network motifs can help understand cascade lifecycle stages.

Abstract

A growing set of applications consider the process of network formation by using subgraphs as a tool for generating the network topology. One of the pressing research challenges is thus to be able to use these subgraphs to understand the network topology of information cascades which ultimately paves the way to theorize about how information spreads over time. In this paper, we make the first attempt at using network motifs to understand whether or not they can be used as generative elements for the diffusion network organization during different phases of the cascade lifecycle. In doing so, we propose a motif percolation-based algorithm that uses network motifs to measure the extent to which they can represent the temporal cascade network organization. We compare two phases of the cascade lifecycle from the perspective of diffusion-- the phase of steep growth and the phase of inhibition prior to its saturation. Our experiments on a set of cascades from the Weibo platform and with 5-node motifs demonstrate that there are only a few specific motif patterns with triads that are able to characterize the spreading process and hence the network organization during the inhibition region better than during the phase of high growth. In contrast, we do not find compelling results for the phase of steep growth.