**Invited Speakers**

Marta Sales-Pardo, Rovira i Virgili -

*Inference on networks with multiple layers*

I will present probabilistic models to deal with two types of networks with multiple layers: (1) the multiplex case, in which layers represent different mechanisms of interaction. (2) the colored edge case, in which we could represent interactions of different colors as different layers.

Isabella Gollini, Birkbeck -

*Latent variable models for complex networks: flexible modelling and scalable inference*

Network data arise in a wide range of applications including social and biological sciences. In many cases different relations and/or a large amount of nodal information is available. Nodal attributes and links are often in strong relation. For example, nodes having similar features are more likely to be connected to each other and vice versa. We introduce a new framework of latent variable network models which combine the information given by heterogenous relational network structures in order to analyse, identify latent traits or groups, visualise network data and predict missing links and nodes. Network data are typically of large size and all the likelihood functions of the models proposed cannot be evaluated analytically. To overcome this problem, we adopt a variational approach to estimation which turns out to improve considerably the computational efficiency without a significant loss of accuracy with respect to other existing methods. The effectiveness of this methodology is demonstrated on the analysis of a wide variety of networks (from small to large networks). The analysis is carried out with the lvm4net package for R (available on CRAN).

Daniel Sussman, Harvard -

*Unbiased Estimation of Causal Effects under Network Interference*

In online experiments, frequently the treatment of one unit may cause effects on neighboring units according to a network structure. In this talk, we investigate a series of assumptions about network interference for causal inference. We will answer the question of when unbiased estimators exist and provide a method to choose among them.