| Title | Internet Device Graphs |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Malloy, Matthew, Barford, Paul, Alp, Enis Ceyhun, Koller, Jonathan, Jewell, Adria |
| Conference Name | Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4887-4 |
| Keywords | composability, device, edge detection, graphs, Internet, Metrics, pubcrawl, resilience, Resiliency, Scalability, security |
| 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 United 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. Next, we apply community detection algorithms to the graph resulting in a partitioning of internet devices into 100 million small communities representing physical households. We validate this partition with a unique ground truth dataset. We report on the characteristics of the graph and the communities. Lastly, we discuss the important issues of ethics and privacy that must be considered when creating and studying device graphs, and suggest further opportunities for device graph enrichment and application. |
| URL | http://doi.acm.org/10.1145/3097983.3098114 |
| DOI | 10.1145/3097983.3098114 |
| Citation Key | malloy_internet_2017 |
Internet Device Graphs