Sampling of User Behavior Using Online Social Network

Accurate Online Social Network User Profiling

We present PGPI+ (Partial Graph Profile Inference+) an improved algorithm for user profiling in online social networks. PGPI+ infers user profiles under the constraint of a partial social graph using rich information about users (e.g. group memberships, views and likes) and handles nominal and numeric attributes. Experimental results with 20,000 user profiles from the Pokec social network shows...

assessment of the efficiency of s.p.g.c refineries using network dea

data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...

Online Network Revenue Management using Thompson Sampling

Characterizing Group-Level User Behavior in Major Online Social Networks

In this paper, we conduct a detailed measurement study to characterize and compare the “group-level”behavior of users in Facebook, Twitter and Google+. We focus on Popular, Cross (with account in three OSNs) and Random group of users in each OSN since they offer complementary views. We capture user behavior with the following metrics: user connectivity, user activity and user reactions. Our gro...

Towards Detecting Anomalous User Behavior in Online Social Networks

Users increasingly rely on crowdsourced information, such as reviews on Yelp and Amazon, and liked posts and ads on Facebook. This has led to a market for blackhat promotion techniques via fake (e.g., Sybil) and compromised accounts, and collusion networks. Existing approaches to detect such behavior relies mostly on supervised (or semi-supervised) learning over known (or hypothesized) attacks....