2013/05/15 by Manuel Gomez-Rodriguez, Manuel Gomez Rodriguez, Rodriguez, Manuel Gomez +4 · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #Error Correcting Code Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.DS #cs.SI #physics.soc-ph #stat.ML
paper · pdf · doi:10.48550/arxiv.1305.3616
To appear at ICML '13
arxiv created 2013/05/15 · openalex publication_date 2013/05/15 · arxiv updated 2013/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.