Phantom cascades: The effect of hidden nodes on information diffusion

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication

Submodular Inference of Diffusion Networks from Multiple Trees

Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.Comment: To appear in the 29th International Conference on Machine Learning (ICML), 2012. Website: http://www.stanford.edu/~manuelgr/network-inference-multitree

Muon-Induced Background Study for Underground Laboratories

We provide a comprehensive study of the cosmic-ray muon flux and induced activity as a function of overburden along with a convenient parameterization of the salient fluxes and differential distributions for a suite of underground laboratories ranging in depth from $\sim$1 to 8 km.w.e.. Particular attention is given to the muon-induced fast neutron activity for the underground sites and we develop a Depth-Sensitivity-Relation to characterize the effect of such background in experiments searching for WIMP dark matter and neutrinoless double beta decay.Comment: 18 pages, 28 figure

Mathematics of complexity in experimental high energy physics

Mathematical ideas and approaches common in complexity-related fields have been fruitfully applied in experimental high energy physics also. We briefly review some of the cross-pollination that is occurring.Comment: 7 pages, 3 figs, latex; Second International Conference on Frontier Science: A Nonlinear World: The Real World, Pavia, Italy, 8-12 September 200