Internet Device Graphs

Matthew Malloy (comScore);Paul Barford (comScore, University of Wisconsin);Enis Ceyhun Alp (University of Wisconsin);Jonathan Koller (comScore);Adria Jewel (comScore)

Abstract

Internet device graphs identify relationships between user-centric internet connected devices such as desktops, laptops, smartphones, tablets, gaming consoles, TV’s, etc. The ability to create such graphs is compelling for online advertising, content customization, recommendation systems, security and operations. We begin by describing an algorithm for generating a device graph based on IP-colocation, and then apply the algorithm to a corpus of over 2.5 trillion internet events collected over the period of six weeks in the Unitied States. The resulting graph exhibits immense scale with greater than 7.3 billion edges (pair-wise relationships) between more than 1.2 billion nodes (devices), accounting for the vast majority of internet connected devices in the US 1 . Next, we apply community detection algorithms to the graph resulting in a partitioning of internet devices into 100 million small communities