Using Users' Activity Metrics for Link-Prediction in a Large Online Social Network

A considerable amount of recent research has been conducted on the link-prediction problem, that is the problem of accurately predicting edges that will be established between actors of a social network in a future time period [LNK07, LZ10]. In cooperation with the provider of a German social network site (SNS), we aim to contribute to this line of research by analyzing the link-formation and interaction patterns of approximately 9.38 million members of one of the largest German online social networks (OSN). It is our goal to understand the role users' activity levels play in link-prediction based on local structural similarity metrics. Such metrics estimate the likelihood of future link-formation between actors based on the structure of their common neighborhoods, i.e. the networks comprised of their mutual acquaintances. Neighborhood-based metrics are usually applied to a SNS's social graph, which is comprised of the corresponding users and their unweighted mutual connections, which are often referred to as friendships [WBS + 09]. Unfortunately, the social graph is not necessarily a good predictor of strong relationships between actors [WBS + 09], as it neglects much of the information usually provided by a SNS – especially activity-related information such as private and public user interaction. Furthermore, the social graph neither allows for the differentiation between recently established and long-time relationships, nor is it designed to reflect the intensity of relationships between actors. We argue that a consideration of both the temporal nature and the intensity of relationships could be used to improve the link-prediction performance of neighborhood-based similarity metrics. Therefore, we propose applying weighted versions of well-known neighborhood-based similarity metrics (i.e. Attachment) to a combination of the unweighted social graph and a weighted graph derived from actors' recent communication activities. Furthermore, we have developed and evaluateed a set of custom metrics to capture the activity of actors' common neighborhoods. To 1175