Our social interactions are often made by electronic means, and are
thus recorded in accessible formats. This opens a range of possibilities
for studying human interactions from data such as social network sites on
the web and cell phone communications. These tasks usually involve massive
amount of data (billions of records) that is often quite noisy and even
corrupted. Social and artificial networks suggest new challenges in
modeling machine learning problems as there are strong spatial and temporal
correlations. In this workshop we will hear researchers from academia and
industry share their insights about the theory and applications of social analytics.
Our focus will be on graph mining and predictive tools that can be used in
this area. We will also consider different applications from recommender
systems, marketing, search, network optimization and other emerging areas.