Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with a small number of individuals, and because the structure of these interactions matters for spreading processes, the pairwise relationships between individuals in a population can be usefully represented by a network. Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social / contact network in an Indian village and an online social network in the U.S. Our goal is to learn simultaneously about the spreading process parameters and the source node (first infected node) of the epidemic, given a fixed and known network structure, and observations about state of nodes at several points in time. Our inference scheme is based on approximate Bayesian computation (ABC), an inference technique for complex models with likelihood functions that are either expensive to evaluate or analytically intractable. ABC enables us to adopt a Bayesian approach to the problem despite the posterior distribution being very complex. Our method is agnostic about the topology of the network and the nature of the spreading process. It generally performs well and, somewhat counter-intuitively, the inference problem appears to be easier on more heterogeneous network topologies, which enhances its future applicability to real-world settings where few networks have homogeneous topologies.
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks have a power-law distribution of the vertices which in turn implies the number of edges scale slower than quadratically in the number of vertices. These assumptions are fundamentally irreconcilable as the Aldous-Hoover theorem implies quadratic scaling of the number of edges. Recently Caron and Fox (2014) proposed the use of a different notion of exchangeability due to Kallenberg (2009) and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re-introduce the use of block-structure for network models obeying Kallenberg's notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model is not significantly more difficult to implement than existing approaches to block-modelling and performs well on real network datasets.
News concerning Artificial Intelligence (AI) abounds again. The progress with Deep Learning techniques are quite remarkable with such demonstrations of self-driving cars, Watson on Jeopardy, and beating human Go players. This rate of progress has led some notable scientists and business people to warn about the potential dangers of AI as it approaches a human level. Exascale computers are being considered that would approach what many believe is this level. However, there are many questions yet unanswered on how the human brain works, and specifically the hard problem of consciousness with its integrated subjective experiences.
Watson, IBM's supercomputer, is most well known for beating two quizmasters on popular quiz show Jeopardy! in 2011. The impressive artificially intelligent software was developed to advance machine learning capabilities, including natural language processing, reasoning and knowledge retrieval. Watson can access information from an endless list of sources, from literature to databases. As AI continues to attract investment and R&D, it will impact our lives in so many ways. It's not surprising, then, that Watson has rather expanded its repertoire since its Jeopardy!
Within only a few years after the launch of video sharing platforms, viral videos have become a pervasive Internet phenomenon. Yet, notwithstanding growing scholarly interest, the suitability of the viral metaphor seems not to have been studied so far. In this paper, we therefore investigate the attention dynamics of viral videos from the point of view of mathematical epidemiology. We introduce a novel probabilistic model of the progression of infective diseases and use it to analyze time series of YouTube view counts and Google searches. Our results on a data set of almost 800 videos show that their attention dynamics are indeed well accounted for by our epidemic model. In particular, we find that the vast majority of videos considered in this study show very high infection rates.