Patterns of Influence in a Recommendation Network

Abstract

Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks. We investigate a large person-to-person recommendation network, consisting of four million people who made sixteen million recommendations on half a million products. Such a dataset allows us to pose a number of fundamental questions: What kinds of cascades arise frequently in real life? What features distinguish them? We enumerate and count cascade subgraphs on large directed graphs; as one component of this, we develop a novel efficient heuristic based on graph isomorphism testing that scales to large datasets. We discover novel patterns: the distribution of cascade sizes is approximately heavy-tailed; cascades tend to be shallow, but occasional large bursts of propagation can occur. The relative abundance of different cascade subgraphs suggests subtle properties of the underlying social network and recommendation process.

Work partially supported by the National Science Foundation under Grants No. IIS-0209107 IIS-0205224 INT-0318547 SENSOR-0329549 EF-0331657 IIS-0326322 CCF-0325453, IIS-0329064, CNS-0403340, CCR-0122581, a David and Lucile Packard Foundation Fellowship, and also by the Pennsylvania Infrastructure Technology Alliance (PITA). This publication only reflects the authors’ views.