Learning And Inference In Massive Social Networks
Source: New York University
Researchers and practitioners increasingly are gaining access to data on explicit social networks. For example, telecommunications and technology firms record data on consumer networks (via phone calls, emails, voice-over-IP, instant messaging), and social-network portal sites such as MySpace, Friendster and Facebook record consumer-generated data on social networks. Inference for fraud detection [5, 3, 8], marketing , and other tasks can be improved with learned models that take social networks into account and with collective inference , which allows inferences about nodes in the network to affect each other. However, these socialnetwork graphs can be huge, comprising millions to billions of nodes and one or two orders of magnitude more links.