Lucca, Italy, Wednesday, September 24,
Sala dell’Affresco (San Micheletto)
Luis M. Rocha
Redundancy, control and collective computation in network dynamics
The structure of networks has provided many insights into the organization of complex systems. The success of this approach is its ability to capture the organization of multivariate interactions, and how it changes in time (network evolution) without explicit dynamical rules for node variables. As the field matures, however, there is a need to move from understanding to controlling complex systems. This is particularly true in systems biology and medicine, where increasingly accurate models of biochemical regulation have been produced. More than understanding the organization of biochemical regulation, we need to derive control strategies that allow us, for instance, to move a mutant cell to a wild-type state, or revert a mature cell to a pluripotent state. In this talk I will highlight ongoing work in our group aimed at supporting this goal: 1) identification of redundant edges in network evolution via the computation of the metric backbone of weighted graphs; 2) a study of the relationship between network structure and controllability through the analysis of dynamical ensembles of BN; 3) an outline of our schema redescription methodology, used to extract the canalizing core of automata network dynamics, thus simplifying the characterization of control in large models of natural networks, such as models biochemical regulation; 4) a study demonstrating that canalization (measured as effective connectivity) is an order parameter of Boolean network dynamics.
Many real-world data are naturally modeled as link streams, i.e. series of triplets (t,a,b) indicating that an interaction between a and b occurred at time t. Typical examples include phone calls, email exchanges, file or money transfers, citations, and many others. Modeling such data as networks captures their structural features, while modeling them as time series captures their dynamics. In order to capture both aspects, network science has recently developed several approaches like temporal networks, time-varying graphs, or dynamic graphs. Although they already provide much insight, these approaches still poorly capture the both structural and temporal nature of link streams. We propose instead to consider link streams as a new family of objects, and to define concepts for studying them directly, without resorting to graphs. This language would play for link streams a role similar to the one played by graph theory for networks. I present in this talk our first steps towards such a language, and the reasons why this approach seems promising.
The network controllability is determined by low in-degree and out-degree nodes
The controllability of the dynamical state of a network is central in network theory and has wide applications ranging from network medicine to financial markets. The driver nodes of the network are the nodes that can bring the network to the desired dynamical state if an external signal is applied to them. Using the framework of structural controllability, here it will be shown that the density of nodes with in-degree and out-degree equal to 0,1 and 2 determines the number of driver nodes in the network. Moreover, networks with minimum in-degree and out-degree greater than 2, are shown to be always fully controllable by an infinitesimal fraction of driver nodes, regardless on the other properties of the degree distribution. Finally, based on these results, an algorithm to improve the controllability of networks is proposed.
large scale social phenomena
large scale social phenomena
Online social networks have changed the way in which we communicate. These networks are characterized by a competitive information flow and a dynamic topology, where most of the mechanisms leading to structural and dynamical changes are yet to be discovered. In this talk, we analyze data from a microblogging service to address the question of whether consensus and collective attention can be anticipated. To this end, we first propose a new methodology based on the analysis of temporal series to unravel who are the drivers of a social movement and when a transition from a fragmented phase to a global scale movement can take place. Finally, we also discuss a new methodology that allow studying online social systems as an ecosystem.
Using opinion dynamics for probing cultural spaces
Authors: Alexandru-Ionut Babeanu, Leandros Talman,
An interesting challenge arising when studying
social systems is understanding how these provide favorable
conditions for both collective behavior on the short term and
cultural diversity on the long term. At a formal level, this
problem has recently been solved, by showing that both aspects
are present if the non-trivial properties of the underlying
``cultural space'' are taken into account. A ``cultural space''
is the set of pairwise "cultural distances" for a given sample
of individuals, which are in turn directly related to the
pairwise overlaps in their measured opinions with respect to a
given set of issues. An empirical cultural space can be
constructed from large-scale survey data. It has been shown
that, when used as input for two types of models of opinion
dynamics, one providing a measure of short term collective
behavior and one a measure of long term cultural diversity,
the empirical space behaves very differently than a randomly generated space, signaling that the former has interesting structural properties. However, this has been explicitly shown only for one source of data. By repeating the same analysis for cultural spaces obtained from multiple empirical sources, we provide extensive evidence for the ubiquity of these structural properties. Building on previous work, we also develop and study models for artificially generating cultural spaces that would reproduce these results.
Controlling Information Flow in Social Networks