The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the scores obtained through executive opinion surveys that constitute the WEF's Global Competitiveness Index in conjunction with other country-level metrics and indicators to identify actionable levers of innovation. The findings can help country leaders and organizations shape the policies to drive developmental activities and increase the capacity of innovation.

Despite the hardware news this week, E3 2016 is still all about the games. For gamers, that means a bounty of new trailers revealing gameplay and cinematic introductions to new worlds. Whether you're a fan of the PC, Xbox One or PlayStation, it's hard not to get excited about what's ahead. This may be the most buzzed-about trailer coming out of E3 2016. The first game from "Metal Gear Solid" creator Hideo Kojima after his departure from Konami was always going to be a big deal, but then you add some really interesting visuals and a naked Norman Reedus, better known as Daryl from "The Walking Dead," holding a baby and that's a recipe for success.

Once a year, when Slava Epstein was growing up in Moscow, his mother took him to the Exhibition of the Achievements of the National Economy, a showcase for the wonders of Soviet life. The expo featured many things--from industrial harvesters to Uzbek wine--but Epstein, who began going in the nineteen-sixties, when he was eight or nine, was interested primarily in one: the Cosmos Pavilion, a building the size of a hangar, with a ceiling shaped like a giant inverted parabola. Space fever was running high in the city. Since 1961, when Yuri Gagarin orbited the globe, unmanned vessels had been launched toward Mars and Venus. Beside the expo's entrance, the towering Monument to the Conquerors of Space depicted a probe swooping up to the heavens. The Pavilion displayed futuristic technology--Vostok rockets and Soyuz orbiters--but Epstein was less interested in the glories of advanced thruster design than in the glories of space. He wanted to devote himself to astronomy. When a textbook that he found on the topic began with algebraic formulas, he prodded his older brother to explain them. During high school, he enrolled in classes in physics and math at Moscow State University. His parents disapproved of his desired career: because he is half Jewish, Epstein would face harsh Soviet quotas limiting Jews in the study of physics, a field deemed relevant to national security. But after his first lecture the professor invited him for a walk, and affirmed what they had been saying all along. "Don't do it," he warned. Soviet Russia may have been a fatalist's paradise, but from a young age Epstein felt that he was hardwired for optimism. He convinced himself that what is truly important in science is the ability to connect ideas, no matter the field, and so he took up biology. Rather than telescopes, he would use microscopes, which he began taking with him on trips to the White Sea, near the Arctic Circle, to study protozoa along the shore--research that could be conducted with minimal state interference. Over time, he grew interested in even smaller, more ancient forms of life: bacteria. Studying microbes inevitably causes a reordering of one's perceptions: for more than two billion years, they were the only life on this planet, and they remain in many ways its dominant life form. To a remarkable extent, the microbial cosmos was less explored than the actual cosmos: precisely how the organisms evolve, replicate, fight, and communicate remains unclear. Nearly all of microbiology, Epstein eventually learned, was built on the study of a tiny fraction of microbial life, perhaps less than one per cent, because most bacteria could not be grown in a laboratory culture, the primary means of analyzing them. By the time he matured as a scientist, many researchers had given up trying to cultivate new species, writing off the majority as "dark matter"--a term used in astronomy for an inscrutable substance that may make up most of the universe but cannot be seen.

Filmmaker Oscar Sharp pulled together a cast and crew for a day of filming. He had actors, including Thomas Middleditch from HBO's "Silicon Valley." He had a set, props and special effects. Most importantly, he had a script written by an artificial-intelligence neural network. And the finished product, "Sunspring," is exactly as weird as you'd expect.

Filmmaker Oscar Sharp pulled together a cast and crew for a day of filming. He had actors, including Thomas Middleditch from HBO's "Silicon Valley." He had a set, props and special effects. Most importantly, he had a script written by an artificial-intelligence neural network. And the finished product, "Sunpring," is exactly as weird as you'd expect.

In this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.

The security threat landscape continues to evolve not just in scale, but, more importantly, in sophistication. Despite a range of advancements in the industry to safeguard against increasingly bold and intricate threats, organizations have struggled to keep pace with the technologies and techniques employed by those responsible for such attacks. As companies continue to increase their digital footprints, "identify and diagnose" capabilities are not enough to remediate against a growing fundamental business challenge for organizations of all shapes and sizes. We spoke with Avivah Litan, vice president and distinguished analyst at Gartner, about the development of advanced security analytics and important considerations for organizations looking to implement machine learning to defend against an array of internal and external security threats. Q: How are analytics and machine learning changing the current security landscape?

In 10 years, we'll see big changes in how people live their lives and how companies operate, thanks to the innovation that's now being kindled around the world. So says Alec Ross, former innovation adviser to Hillary Clinton during her term as U.S. Secretary of State, who lays out the thesis in his new book, The Industries of the Future. In Part 1 of our interview, Ross explains how the technology underlying the cryptocurrency Bitcoin, blockchain technology, will greatly reduce the friction in financial and other transactions. Here he discusses the impact of cognitive robots and the outlook for U.S. companies trying to compete on a global scale. You talked about robotics a lot in your book and you focused for the most part on physical robots.

In Panama, indigenous tribes are turning to a modern tool to help protect their homes: drones. Vast rainforests, which once covered more than half of Panama's land surface, are shrinking – eaten away by development, both official and unofficial. Forest land is becoming mines, hydroelectric projects, farmland, cattle habitat, and the site of illegal logging. In response, seven indigenous tribes, whose members live in autonomous zones known as comarcas, have begun sending up drones to keep an eye on their forests. Three members from each tribe received a month of training on how to use the drones, Reuters reports.