Exploiting Synchronicity Networks for Finding Valuables in Heterogeneous Networks

Successful enterprises depend on high performing teams consisting of productive individuals, who can effective find valuable information. Predictive methods are highly desired to identify these inter-related, multityped entities that can potentially help to improve enterprise performance before they become productive. For such predictive methods, observation of their dynamic behavior in the organizational social networks may provide useful input. In this paper, we propose a novel approach to analyze and rank heterogeneous objects in multi-level networks by their value for improving productivity. Compared to existing approaches that either focus on static factors of productivity or single type of entities (e.g., individuals), our work offers two unique contributions. First, we propose a novel multi-level synchronicity network representation which allows us to exploit the structural characteristics of various entities’ dynamic behavior. Furthermore, based on the synchronicity networks, we propose a novel algorithm for Heterogeneous Multi-level networks Ranking, to simultaneously rank inter-related heterogeneous entities (e.g., topics, individuals and teams) by their value. Our experiments demonstrate that our approach significantly outperforms existing methods in both enterprise organizational social networks and public social media.