09:00 to 09:50 Registration 09:50 to 10:00 Welcome from John Toland (INI Director) INI 1 10:00 to 10:40 Yi Yu Estimating whole brain dynamics using spectral clustering The estimation of time-varying networks for functional Magnetic Resonance Imaging (fMRI) data sets is of increasing importance and interest. In this work, we formulate the problem in a high-dimensional time series framework and introduce a data-driven method, namely Network Change Points Detection (NCPD), which detects change points in the network structure of a multivariate time series, with each component of the time series represented by a node in the network. NCPD is applied to various simulated data and a resting-state fMRI data set. This new methodology also allows us to identify common functional states within and across subjects. Finally, NCPD promises to offer a deep insight into the large-scale characterisations and dynamics of the brain.  This is joint work with Ivor Cribben (Alberta School of Business). INI 1 10:40 to 11:00 Pariya Behrouzi Detecting Epistatic Selection in the Genome of RILs via a latent Gaussian Copula Graphical Model Recombinant Inbred Lines (RILs) derived from divergent parental lines can display extensive segregation distortion and long-range linkage disequilibrium (LD) between distant loci on same or different chromosomes. These genomic signatures are consistent with epistatic selection having acted on entire networks of interacting parental alleles during inbreeding. The reconstruction of these interaction networks from observations of pair-wise marker-marker correlations or pair-wise genotype frequency distortions is challenging as multiple testing approaches are under-powered and true long-range LD is difficult to distinguish from drift, particularly in small RIL panels. Here we develop an efficient method for reconstructing an underlying network of genomic signatures of high-dimensional epistatic selection from multi-locus genotype data. The network captures the conditionally dependent short- and long-range LD structure of RIL genomes and thus reveals "aberrant" marker-marker associations that are due to epistatic selection rather than gametic linkage. The network estimation relies on penalized Gaussian copula graphical models, which accounts for large number of markers p and small number of individuals n. A multi-core implementation of our algorithm makes it feasible to estimate the graph in high-dimensions (max markers ~ 3000). We demonstrate the efficiency of the proposed method on simulated datasets as well as on genotyping data in A.thaliana and Maize. INI 1 11:00 to 11:30 Morning Coffee 11:30 to 12:10 Ginestra Bianconi Multiplex networks Multiplex networks describe interacting systems where the same set of nodes are linked by different type of interactions. Multiplex networks include social networks, infrastructures and biological systems. Characterizing and modelling the structure of multiplex networks is fundamental for solving network inference problems. Here we will discuss recent results showing that multiplex networks encode more information than their single layers taken in isolation. In fact they are characterized by strongly correlated structures that reveal important statistical properties of the complex system that they describe. INI 1 12:10 to 12:30 Mirko Signorelli Modelling community structure in the Italian Parliament: a penalized inference approach In many parliamentary systems, bills can be proposed by a single parliamentarian, or cosponsored by a group of parliamentarians. In the latter case, bill cosponsorship defines a symmetric relation that can be taken as a measure of ideological agreement between parliamentarians.   Political scientists have often analysed bill cosponsorship networks in the US Congress, assessing its community structure and the behaviour of minorities therein. In this talk, I will consider data on bill cosponsorship in the Italian Chamber of Deputies over the last 15 years. If compared to the US Congress, a distinguishing feature of the Italian Chamber is the presence of a large number of political groups: the primary purpose of the analysis is thus to infer the pattern of collaborations between these groups.   We consider a stochastic blockmodel for edge-valued graphs that views bill cosponsorship as the result of a Poisson process, which explicitly depends on membership of parliamentary groups. As the number of model parameters increases quickly with the number of groups, we pursue a penalized likelihood approach to model estimation that enables us to infer a sparse reduced graph, which summarizes relations between parliamentary groups.   Besides showing the effects of gender and geographic proximity on bill cosponsorship, the analysis points out the evolution from a highly polarized political arena, in which Deputies base collaborations on their identification with left or right-wing values, towards an increasingly fragmented Parliament, where a rigid separation of political groups into coalitions does not seem to hold any more, and collaborations beyond the perimeter of coalitions become possible.Joint work with Ernst Wit.Related links: https://arxiv.org/abs/1607.08743 (arXiv preprint) INI 1 12:30 to 13:30 Lunch @ Wolfson Court 13:30 to 14:10 Matteo Barigozzi Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series Co-author: Marc Hallin (ECARES-ULB ) We consider weighted directed networks for analysing large panels of financial volatilities.For a given horizon $h$, the weight associated with edge $(i,j)$ represents the $h$-step-ahead forecast error variance of variable $i$ accounted for by variable $j$ innovations. To challenge the curse of dimensionality, we decompose the panel into a factor (market) driven component and an idiosyncratic one modelled by means of a sparse VAR. Inversion of the VAR together with suitable identification restrictions, produce the estimated network, bymeans of which we can assess how systemic each firm is. An analysis of the U.S. stock market demonstrates the prominent role of Financial firms as source of contagion during the 2007-2008 crisis. INI 1 14:10 to 14:50 Daniele Durante Bayesian modeling of networks in complex business intelligence problems Co-authors: Sally Paganin (University of Padova, Dept. of Statistical Sciences), Bruno Scarpa (University of Padova, Dept. of Statistical Sciences), David B. Dunson (Duke University, Dept. of Statistical Science) Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specific products, along with co-subscription networks encoding multiple purchasing behavior. Data are available for several agencies within the same insurance company, and our goal is to efficiently exploit co-subscription networks to inform targeted advertising of cross-sell strategies to currently mono-product customers. We address this goal by developing a Bayesian hierarchical model, which clusters agencies according to common mono-product customer choices and co-subscription networks. Within each cluster, we efficiently model customer behavior via a cluster-dependent mixture of latent eigenmodels. This formulation provides key information on mono-product customer choices and multiple purchasing behavior within each cluster, informing targeted cross-sell strategies. We develop simple algorithms for tractable inference, and assess performance in simulations and an application to business intelligence Related Links http://arxiv.org/abs/1510.00646 - Arxiv version of the manuscript INI 1 14:50 to 15:30 Luca De Benedictis Implementing Propensity Score Matching with Network Data: The effect of GATT on bilateral trade Co-authors: Bruno Arpino, Alessandra Mattei   Motivated by the evaluation of the causal effect of the General Agreement on Tariffs and Trade on bilateral international trade flows, we investigate the role of network structure in propensity score matching under the assumption of strong ignorability. We study the sensitivity of causal inference with respect to the presence of characteristics of the network in the set of confounders conditional on which strong ignorability is assumed to hold. We find that estimates of the average causal effect are highly sensitive to the presence of node-level network statistics in the set of confounders. Therefore, we argue that estimates may suffer from omitted variable bias when the relational dimension of units is ignored, at least in our application. INI 1 15:30 to 16:00 Afternoon Tea 16:00 to 16:20 Nynke Niezink Modeling the dynamics of social networks and continuous actor attributes Co-authors: Tom Snijders (University of Groningen)   Social networks and the characteristics of the actors who constitute these networks are not static; they evolve interdependently over time. People may befriend others with similar political opinions or change their own opinion based on that of their friends. The stochastic actor-oriented model is used to statistically model such dynamics. We will present an extension of this model for continuous dynamic actor characteristics. The method available until now assumed actor characteristics to be measured on an ordinal categorical scale, which yielded practical limitations for applied researchers. We now model the interdependent dynamics by a stochastic differential equation for the attribute evolution and a Markov chain model for the network evolution. Although the model is too complicated to calculate likelihoods or estimators in closed form, the stochastic evolution process can be easily simulated. Therefore, we estimate model parameters using the method of moments and the Robbins-Monro algorithm for stochastic approximation. We will illustrate the proposed method by a study of the relation between friendship and obesity, analyzing body mass index as continuous dynamic actor attribute. INI 1 16:20 to 17:00 Katherine McLaughlin Analysis of Networks with Missing Data with Application to the National Longitudinal Study of Adolescent Health Co-authors: Krista J. Gile (University of Massachusetts at Amherst), Mark S. Handcock (University of California, Los Angeles) It is common in the analysis of social network data to assume that it represents a census of the networked population of interest. Often the data result from sampling of the networked population via a known mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this talk we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We show the value of the mean value parametrization to study differences between modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. The work presented is by Krista J. Gile and Mark S. Handcock. INI 1 19:30 to 22:00 Formal Dinner at Corpus Christi College