Diffusion processes are central to human interactions. Despite extensive studies that span multiple disciplines, our knowledge is limited to spreading processes in non-substitutive systems. Yet, a considerable number of ideas, products and behaviors spread by substitution-to adopt a new one, agents must give up an existing one. Here, we find that, ranging from mobile handsets to automobiles to smart phone apps, early growth patterns in substitutive systems follow a power law with non-integer exponents, in sharp contrast to the exponential growth customary in spreading phenomena. Tracing 3.6 million individuals substituting for mobile handsets for over a decade, we uncover three generic ingredients governing substitutive processes, allowing us to develop a minimal substitution model, which not only predict analytically the observed growth patterns, but also collapse growth trajectories of constituents from rather diverse systems into a single universal curve. These results demonstrate that the dynamics of complex substitutive systems are governed by robust self-organizing principles that go beyond the particulars of individual systems, which implies that these results could guide the understanding and prediction of all spreading phenomena driven by substitutions, from electric cars to scientific paradigms, from renewable energy to new healthy habits.

 

Universal Scaling in Complex Substitutive Systems
Ching Jin, Chaoming Song, Johannes Bjelland, Geoffrey Canright, Dashun Wang

Source: arxiv.org

Observational data about human behavior is often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson’s paradox, whereby the trends observed in data that has been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson’s paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson’s paradox is affecting results of analysis. The presence of Simpson’s paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies’ findings.

 

Computational Social Scientist Beware: Simpson’s Paradox in Behavioral Data
Kristina Lerman

Source: arxiv.org

Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.

 

Generating realistic scaled complex networks
Christian L. Staudt, Michael Hamann, Alexander Gutfraind, Ilya Safroand Henning Meyerhenke
Applied Network Science 2017 2:36
https://doi.org/10.1007/s41109-017-0054-z

Source: appliednetsci.springeropen.com