title: Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay.
abstract: Most social networking services support multiple types of relationships between users, such as getting connected, sending messages, and consuming feed updates. These users and relationships can be naturally represented as a dynamic multi-view network, which is a set of weighted graphs with shared common nodes but having their own respective edges. Different network views, representing structural relationship and interaction types, could have very distinctive properties individually and these properties may change due to interplay across views. Therefore, it is of interest to study how multiple views interact and affect network dynamics and, in addition, explore possible applications to social networking. In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects. Through our proposed descriptors, we observe the synergy and cannibalization between different user groups and network views from LinkedIn dataset. We then develop models that consider the synergy and cannibalization per new relationship, and show the outperforming predictive capability of our models compared to baseline models. Finally, the proposed models allow us to understand the interplay among different views where they dynamically change over time.
authors.affiliation: Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Shi
authors.affiliation: LinkedIn Corporation, Mountain View, CA USA.
authors.forename: Myunghwan
authors.initials: M
authors.lastname: Kim
authors.affiliation: LinkedIn Corporation, Mountain View, CA USA.
authors.forename: Shaunak
authors.initials: S
authors.lastname: Chatterjee
authors.affiliation: LinkedIn Corporation, Mountain View, CA USA.
authors.forename: Mitul
authors.initials: M
authors.lastname: Tiwari
authors.affiliation: LinkedIn Corporation, Mountain View, CA USA.
authors.forename: Souvik
authors.initials: S
authors.lastname: Ghosh
authors.affiliation: LinkedIn Corporation, Mountain View, CA USA.
authors.forename: Rómer
authors.initials: R
authors.lastname: Rosales
keywords: Multi-view networks; network dynamics; social networks