Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND

Abstract

There is an emerging trend in the Chinese automobile industries that automakers are introducing exclusive enterprise social networks (EESNs) to expand sales and provide after-sale services. The traditional online social networks (OSNs) and enterprise social networks (ESNs), such as Twitter and Yammer, are ingeniously designed to facilitate unregulated communications among equal individuals. However, users in EESNs are naturally social stratified, consisting of both enterprise staffs and customers. In addition, the motivation to operate EESNs can be quite complicated, including providing customer services and facilitating communication among enterprise staffs. As a result, the social behaviors in EESNs can be quite different from those in OSNs and ESNs. In this work, we aim to analyze the social behaviors in EESNs. We consider the Chinese car manufacturer NIO as a typical example of EESNs and provide the following contributions. First, we formulate the social behavior analysis in EESNs as a link prediction problem in heterogeneous social networks. Second, to analyze this link prediction problem, we derive plentiful user features and build multiple meta-path graphs for EESNs. Third, we develop a novel Fast (H)eterogeneous graph (A)ttention (N)etwork algorithm for (D)irected graphs (FastHAND) to predict directed social links among users in EESNs. This algorithm introduces feature group attention at the node-level and uses an edge sampling algorithm over directed meta-path graphs to reduce the computation cost. By conducting various experiments on the NIO community data, we demonstrate the predictive power of our proposed FastHAND method. The experimental results also verify our intuitions about social affinity propagation in EESNs.