We study the dynamic network of real world person-to-person interactions between approximately 1,000 individuals with 5-min resolution across several months. There is currently no coherent theoretical framework for summarizing the tens of thousands of interactions per day in this complex network, but here we show that at the right temporal resolution, social groups can be identified directly. We outline and validate a framework that enables us to study the statistical properties of individual social events as well as series of meetings across weeks and months. Representing the dynamic network as sequences of such meetings reduces the complexity of the system dramatically. We illustrate the usefulness of the framework by investigating the predictability of human social activity.

 

Fundamental structures of dynamic social networks
Vedran Sekara, Arkadiusz Stopczynski, and Sune Lehmann

PNAS vol. 113 no. 36

Source: www.pnas.org

Spontaneous synchronization has long served as a paradigm for behavioral uniformity that can emerge from interactions in complex systems. When the interacting entities are identical and their coupling patterns are also identical, the complete synchronization of the entire network is the state inheriting the system symmetry. As in other systems subject to symmetry breaking, such symmetric states are not always stable. Here, we report on the discovery of the converse of symmetry breaking—the scenario in which complete synchronization is not stable for identically coupled identical oscillators but becomes stable when, and only when, the oscillator parameters are judiciously tuned to nonidentical values, thereby breaking the system symmetry to preserve the state symmetry. Aside from demonstrating that diversity can facilitate and even be required for uniformity and consensus, this suggests a mechanism for convergent forms of pattern formation in which initially asymmetric patterns evolve into symmetric ones.

 

Symmetric States Requiring System Asymmetry
Takashi Nishikawa and Adilson E. Motter
Phys. Rev. Lett. 117, 114101

Source: journals.aps.org

See Also: Synopsis: Diversity Breeds Conformity

Almost all processes — highly correlated, weakly correlated, or correlated not at all—exhibit statistical fluctuations. Often physical laws, such as the Second Law of Thermodynamics, address only typical realizations — as highlighted by Shannon’s asymptotic equipartition property and as entailed by taking the thermodynamic limit of an infinite number of degrees of freedom. Indeed, our interpretations of the functioning of macroscopic thermodynamic cycles are so focused. Using a recently derived Second Law for information processing, we show that different subsets of fluctuations lead to distinct thermodynamic functioning in Maxwellian Demons. For example, while typical realizations may operate as an engine — converting thermal fluctuations to useful work — even “nearby” fluctuations (nontypical, but probable realizations) behave differently, as Landauer erasers — converting available stored energy to dissipate stored information. One concludes that ascribing a single, unique functional modality to a thermodynamic system, especially one on the nanoscale, is at best misleading, likely masking an array of simultaneous, parallel thermodynamic transformations. This alters how we conceive of cellular processes, engineering design, and evolutionary adaptation.

 

Not All Fluctuations are Created Equal: Spontaneous Variations in Thermodynamic Function
James P. Crutchfield, Cina Aghamohammadi

Source: arxiv.org

Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Renyi q-entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.

 

Spectral entropies as information-theoretic tools for complex network comparison
Manlio De Domenico, Jacob Biamonte

Source: arxiv.org

The community deception problem is about how to hide a target community C from community detection algorithms. The need for deception emerges whenever a group of entities (e.g., activists, police enforcements) want to cooperate while concealing their existence as a community. In this paper we introduce and formalize the community deception problem. To solve this problem, we describe algorithms that carefully rewire the connections of C’s members. We experimentally show how several existing community detection algorithms can be deceived, and quantify the level of deception by introducing a deception score. We believe that our study is intriguing since, while showing how deception can be realized it raises awareness for the design of novel detection algorithms robust to deception techniques.

 

From Community Detection to Community Deception
Valeria Fionda, Giuseppe Pirrò

Source: arxiv.org