In complex systems, the network of interactions we observe between system's components is the aggregate of the interactions that occur through different mechanisms or layers. Recent studies reveal that the existence of multiple interaction layers can have a dramatic impact in the dynamical processes occurring on these systems. However, these studies assume that the interactions between systems components in each one of the layers are known, while typically for real-world systems we do not have that information. Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs), a generalization of single-layer SBMs that considers different mechanisms of layer aggregation. First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate observed network. Because this solution is computationally intractable, we propose an approximation that enables us to verify that multilayer SBMs are more predictive of network structure in real-world complex systems.
Multilayer stochastic block models reveal the multilayer structure of complex networks Toni Valles-Catala, Francesco A. Massucci, Roger Guimera, Marta Sales-Pardo http://arxiv.org/abs/1411.1098
The percolation properties of networks are strongly affected by their topological features. A new study shows that percolation can proceed at different rates in the core and periphery of a complex, clustered network.
Recent empirical studies using large-scale datasets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multi-layered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, sufficient amount of inter-layer correlation is needed to maintain the relationship between topology and link weights but these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multi-layer WSN model, where the indirect inter-layer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.
Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.
Evolutionary dynamics of time-resolved social interactions Phys. Rev. E 90, 052825 – Published 25 November 2014 Alessio Cardillo, Giovanni Petri, Vincenzo Nicosia, Roberta Sinatra, Jesús Gómez-Gardeñes, and Vito Latora
Progressives know we need to work together beyond our usual “silos,” yet effective collaboration is the exception rather than the rule. Even as movement leaders increasingly realize how important this is, most lack the skills and tools to make it work. Luckily, some very smart people are on the case.
KPI's and goals are fully open and transparent to all employees, which in turn is depolarizing the company. Employees are able to self-select projects or even create new ones with less, or even no, top down approval. Self-organizing teams and decentralized authority for individual employees is the standard. The use of short feedback cycles and goals have a motivating impact for employees and teams. Social technologies are woven into all corners of the organization, using activity streams, file sharing, wikis, task management and even teleprescence robots. Curiosity is supported by embracing experimentation Employees and partners are proud to be a part of an organization due to its higher purpose -- how its products are creating a better world in ecological, health and/or social terms.
In such different domains as statistical physics and spin glasses, neurosciences, social science, economics and finance, large ensemble of interacting individuals taking their decisions either in accordance (mainstream) or against (hipsters) the majority are ubiquitous. Yet, trying hard to be different often ends up in hipsters consistently taking the same decisions, in other words all looking alike. We resolve this apparent paradox studying a canonical model of statistical physics, enriched by incorporating the delays necessary for information to be communicated. We show a generic phase transition in the system: when hipsters are too slow in detecting the trends, they will keep making the same choices and therefore remain correlated as time goes by, while their trend evolves in time as a periodic function. This is true as long as the majority of the population is made of hipsters. Otherwise, hipsters will be, again, largely aligned, towards a constant direction which is imposed by the mainstream choices. Beyond the choice of the best suit to wear this winter, this study may have important implications in understanding dynamics of inhibitory networks of the brain or investment strategies finance, or the understanding of emergent dynamics in social science, domains in which delays of communication and the geometry of the systems are prominent.
The hipster effect: When anticonformists all look the same Jonathan Touboul
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