document: 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 information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space . We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web . These sites tend to have stable circles of influence with more general news media sites acting as connectors between them .
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