Inferring networks of diffusion and influence

Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing ...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Manuel Gomez-Rodriguez, Jure Leskovec, Andreas Krause
• Keywords: Data Mining, Diffusion, KDD 2010, Observing Individual Transmissions, Processes Talking Place