Alessandro Di Stefano

Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory

Nature shows as human beings live and grow inside social structures. This assumption allows us to explain and explore how it may shape most of our behaviours and choices, and why we are not just blindly driven by instincts: our decisions are based on more complex cognitive reasons, based on our connectedness on different spaces. Thus, human cooperation emerges from this complex nature of social network. Our paper, focusing on the evolutionary dynamics, is intended to explore how and why it happens, and what kind of impact is caused by homophily among people. We investigate the evolution of human cooperation using evolutionary game theory on multiplex. Multiplexity, as an extra dimension of analysis, allows us to unveil the hidden dynamics and observe non-trivial patterns within a population across network layers. More importantly, we find a striking role of homophily, as the higher the homophily between individuals, the quicker is the convergence towards cooperation in the social dilemma. The simulation results, conducted both macroscopically and microscopically across the network layers in the multiplex, show quantitatively the role of homophily in human cooperation.

The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks

In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.

Quantifying the propagation of distress and mental disorders in social networks.

Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak.

The bio-inspired and social evolution of node and data in a multilayer network

Following a bio-inspired approach, applied to multilayer social networks, the idea is to build a novel paradigm aimed to improve methodologies and analysis in the Information and Communication Technologies. The social network and the multilayer structure allow to carry out an analysis of the complex patterns, in terms of the dynamics involving the main entities, nodes and data. The nodes represent the basic kernel from which generating ties, interactions, flow of information, influences and action strategies that affect the communities. The data, gathered from multiple sources, after their integration, will become complex objects, enclosing different kinds of information. The proposed approach introduces a level of abstraction that originates from the evolution of nodes and data transformed in “social objects”. This new paradigm consists of a multilayer social network, divided into three layers, generating an increasing awareness, from “things” to “knowledge”, extracting as much “knowledge” as possible. This paradigm allows to redesign the ICT in a bio-networks driven approach.

A Dynamic and Context-Aware Social Network Approach for Multiple Criteria Decision Making Through a Graph-Based Knowledge Learning

Complexity and dynamics characterize a social network and its processes. Social network analysis and graph theory could be used to describe and explore the connectedness among the different entities. Network dynamics further increases the complexity, as each entity with its personal knowledge, cognitive and reasoning capabilities, thinks, decides and acts in a social network, characterized by the heterogeneity of nodes and ties among them. Social network analysis becomes critical to the decision-making process, where a network node will consider both its personal knowledge and the influences received from its neighbors. Network dynamics and the node’s context-awareness affect the relationships among criteria, modifying their ranking in a multiple criteria decision-making process, and hence the decision itself. Thus, the main aim has been to model the decision-making process within a social network, considering both context-awareness and network dynamics. Moreover, we have introduced a process of knowledgetransfer, where the criteria are represented by the knowledge-related values. A Dynamic and Context-Aware Social Network Approach for Multiple Criteria Decision Making Through a GraphBased Knowledge Learning

Bio-Inspired ICT for Big Data Management in Healthcare

In the future, life and death decisions will depend on having more data and more organized knowledge. These data will overcome traditional scale and dimensions, thus we will need to think about new kind of strategies which involve Information and Communication Technologies. Collect, organize and compute every aspect will be crucial for survival of patients and for healthcare management. Following a bio-inspired approach to ICT, we can relate Big Data and the data intensive computing issues in the future vision of a smart healthcare. The multidimensional approach to comorbidity and the introduction of a social dimension of analysis allow to find out correlations and causality relations between diseases and patients also considering the connectedness and social contagion processes. In this way, we obtain an evolution from data to multiagents through the introduction of personalized medicine in a multilayer architecture.

A Energy-Preserving Model for Wireless Sensors Networks Based on Heuristic Self-Organized Routing

One of the main targets related to Wireless Sensor Networks (WSNs) is to reduce power consumption of nodes and of the whole network. An ideal WSN should be networked, scalable, fault-tolerant, energy-aware, and also smart and efficient. Unfortunately, however, this is not always true. The basic idea of the proposal is that senders use a heuristic approach to select the sub-optimal next hop in order to reach just one sink, considering some key requirements such as general performance (QoS and security), efficiency, trust ability, high computational power and energy-aware behaviour. Our model tries to satisfy the need for reaching the nearest sink node, considering a trade-off between the shortest path and heuristic decisions, in a top-level strategy based on a heuristic approach in order to reduce the overall power consumption of nodes of the network.