Inferring Networks of Diffusion and Influence Manuel Gomez-Rodriguez, Jure Leskovec, Andreas Krause Information diffusion and virus propagation are fundamental processes in networks. While it is often possible to directly observe when nodes become infected or adopt information, tracing individual transmissions (i.e., who infects whom) is typically very difficult. Moreover, the underlying network is often unobserved. This paper presents a method for inferring the networks over which contagions propagate by using the times when nodes adopt information or become infected. The method develops an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. The authors demonstrate the effectiveness of their approach by tracing information diffusion 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. They find that the diffusion network of news for the top 1,000 media sites and blogs 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. The paper introduces a scalable algorithm called NETINF for inferring networks of diffusion and influence. The algorithm is based on a probabilistic model of how contagions spread over the edges of the network. The algorithm is efficient and can handle large datasets. The authors show that their approach outperforms a baseline heuristic by an order of magnitude and correctly discovers more than 90% of the edges. The paper also discusses the challenges of inferring networks of diffusion and influence, including the difficulty of tracing individual transmissions and the lack of knowledge about the underlying network. The authors propose a method for inferring the underlying network by using the times when nodes adopt information or become infected. The method is based on a probabilistic model of how contagions spread over the edges of the network. The paper concludes that inferring networks of diffusion and influence is crucial for understanding how information or viruses propagate over networks. By modeling the structure of the propagation network, we can gain insight into the positions and roles various nodes play in the diffusion process and assess the range of influence of nodes in the network.Inferring Networks of Diffusion and Influence Manuel Gomez-Rodriguez, Jure Leskovec, Andreas Krause Information diffusion and virus propagation are fundamental processes in networks. While it is often possible to directly observe when nodes become infected or adopt information, tracing individual transmissions (i.e., who infects whom) is typically very difficult. Moreover, the underlying network is often unobserved. This paper presents a method for inferring the networks over which contagions propagate by using the times when nodes adopt information or become infected. The method develops an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. The authors demonstrate the effectiveness of their approach by tracing information diffusion 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. They find that the diffusion network of news for the top 1,000 media sites and blogs 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. The paper introduces a scalable algorithm called NETINF for inferring networks of diffusion and influence. The algorithm is based on a probabilistic model of how contagions spread over the edges of the network. The algorithm is efficient and can handle large datasets. The authors show that their approach outperforms a baseline heuristic by an order of magnitude and correctly discovers more than 90% of the edges. The paper also discusses the challenges of inferring networks of diffusion and influence, including the difficulty of tracing individual transmissions and the lack of knowledge about the underlying network. The authors propose a method for inferring the underlying network by using the times when nodes adopt information or become infected. The method is based on a probabilistic model of how contagions spread over the edges of the network. The paper concludes that inferring networks of diffusion and influence is crucial for understanding how information or viruses propagate over networks. By modeling the structure of the propagation network, we can gain insight into the positions and roles various nodes play in the diffusion process and assess the range of influence of nodes in the network.