Emanuele Massaro

EPIDEMIC SPREADING AND RISK PERCEPTION IN MULTIPLEX NETWORKS: A SELF-ORGANIZED PERCOLATION METHOD

In this paper we study the interplay between epidemic spreading and risk perception on multiplex networks. The basic idea is that the effective infection probability is affected by the perception of the risk of being infected, which we assume to be related to the fraction of infected neighbors, as introduced by Bagnoli et al. [Phys. Rev. E 76, 061904 (2007)PLEEE81539-375510.1103/PhysRevE.76.061904]. We rederive previous results using a self-organized method that automatically gives the percolation threshold in just one simulation. We then extend the model to multiplex networks considering that people get infected by physical contacts in real life but often gather information from an information network, which may be quite different from the physical ones. The similarity between the physical and the information networks determines the possibility of stopping the infection for a sufficiently high precaution level: if the networks are too different, there is no means of avoiding the epidemics.

Information dynamics algorithm for detecting communities in networks

Abstract The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen’s Markov Cluster algorithm (MCL) method [4] by considering networks’ nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect . The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

A computational toy model for shallow landslides: Molecular dynamics approach

Abstract The aim of this paper is to propose a 2D computational algorithm for modeling the triggering and propagation of shallow landslides caused by rainfall. We used a molecular dynamics (MD) approach, similar to the discrete element method (DEM), that is suitable to model granular material and to observe the trajectory of a single particle, so to possibly identify its dynamical properties. We consider that the triggering of shallow landslides is caused by the decrease of the static friction along the sliding surface due to water infiltration by rainfall. Thence the triggering is caused by the two following conditions: (a) a threshold speed of the particles and (b) a condition on the static friction, between the particles and the slope surface, based on the Mohr–Coulomb failure criterion. The latter static condition is used in the geotechnical model to estimate the possibility of landslide triggering. The interaction force between particles is modeled, in the absence of experimental data, by means of a potential similar to the Lennard-Jones one. The viscosity is also introduced in the model and for a large range of values of the model’s parameters, we observe a characteristic velocity pattern, with acceleration increments, typical of real landslides. The results of simulations are quite promising: the energy and time triggering distribution of local avalanches show a power law distribution, analogous to the observed Gutenberg–Richter and Omori power law distributions for earthquakes. Finally, it is possible to apply the method of the inverse surface displacement velocity [4] for predicting the failure time.

Community-Detection Cellular Automata with Local and Long-Range Connectivity

We explore a community-detection cellular automata algorithm inspired by human heuristics, based on information diffusion and a non-linear processing phase with a dynamics inspired by human heuris- tics. The main point of the methods is that of furnishing different “views” of the clustering levels from an individual point of view. We apply the method to networks with local connectivity and long-range rewiring.

A Cognitive-Inspired Model for Self-Organizing Networks

In this work we propose a computational scheme inspired by the workings of human cognition. We embed some fundamental aspects of the human cognitive system into this scheme in order to obtain a minimization of computational resources and the evolution of a dynamic knowledge network over time, and apply it to computer networks. Such algorithm is capable of generating suitable strategies to explore huge graphs like the Internet that are too large and too dynamic to be ever perfectly known. The developed algorithm equips each node with a local information about possible hubs which are present in its environment. Such information can be used by a node to change its connections whenever its fitness is not satisfying some given requirements. Eventually, we compare our algorithm with a randomized approach within an ecological scenario for the ICT domain, where a network of nodes carries a certain set of objects, and each node retrieves a subset at a certain time, constrained with limited resources in terms of energy and bandwidth. We show that a cognitive-inspired approach improves the overall networks topology better than a randomized algorithm.

Hierarchical Community Structure in Complex (Social) Networks

The investigation of community structure in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science where systems are often represented as graphs. One of the challenges is to find local communities from a local viewpoint in a graph without global information in order to reproduce the subjective hierarchical vision for each vertex. In this paper we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints \cite{Massaro2012}. In this framework we have introduced two more complex heuristics that allow the algorithm to detect the multi-resolution hierarchical community structure of networks from a source vertex or communities adopting fixed values of models parameters. Experimental results show that the proposed methods are efficient and well-behaved in both real-world and synthetic networks.

A cognitive-inspired algorithm for growing networks

We present models for generating different classes of networks by adopting simple local strategies and an original model of the evolutionary dynamics and growth of on-line social networks. The model emulates people’s strategies for acquiring information in social networks, emphasising the local subjective view of an individual and what kind of information the individual can acquire when arriving in a new social context. We assume that the strategy proceeds through two phases: (a) a discovery phase, in which the individual becomes aware of the surrounding world and (b) an elaboration phase, in which the individual elaborates locally the information trough a cognitive-inspired algorithm. Model generated networks reproduce the main features of both theoretical and real-world networks, such as high clustering coefficient, low characteristic path length, strong division in communities, and variability of degree distributions.

Evaluating Cerebral Cortex Connectivity with Local Information Algorithm

Previous works have analyzed the cluster organization of the cat cortical network using both traditional multidimensional scaling methods and evolutionary optimization algorithms. Interestingly, the evolutionary optimization principle of previous works is based on the modularity measure used to find communities in network with global algorithms. In this paper, we deepen this point taking into account different community-detection algorithms. We compare the performances of Net Explorer, a local information dynamics algorithm for detecting communities in networks, with six well-known community detection algorithms: Info map, Hierarchical Info map, Lou vain, Modularity Optimization, Label Propagation and Oslom. The results indicate that Net Explorer is able to detect the four functional clusters where misattributions of some areas are explained by their multimodal function. Results are discussed in terms of misattributions of brain areas to the different clusters emphasizing connections which are explainable (or not) by a cognitive point of view.

Impact of local information in growing networks.

We present a new model of the evolutionary dynamics and the growth of on-line social networks. The model emulates peoples strategies for acquiring information in social networks, emphasising the local subjective view of an individual and what kind of information the individual can acquire when arriving in a new social context. The model proceeds through two phases: (a) a discovery phase, in which the individual becomes aware of the surrounding world and (b) an elaboration phase, in which the individual elaborates locally the information trough a cognitive-inspired algorithm. Model generated networks reproduce main features of both theoretical and real-world networks, such as high clustering coefficient, low characteristic path length, strong division in communities, and variability of degree distributions.

Modeling Epidemic Risk Perception in Networks with Community Structure

We study the influence of global, local and community-level risk perception on the extinction probability of a disease in several models of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular networks, formed by a certain number of random and scale-free communities. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one’s own neighborhood is the most effective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/effectiveness optimum is a tradeoff between several parameters.