Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become ‘stronger’, after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort.
ECCS’14 will be a major international conference and event in the area of complex systems and interdisciplinary science in general. It will offer unique opportunities to study novel scientific approaches in a multitude of application areas. Two days of the conference, 24 and 25 of September, are reserved for satellite meetings, which will cover a broad range of subjects on all aspects of Complex Systems, as reflected by the conference tracks.
Since its first formulations almost a century ago, mathematical models for disease spreading contributed to understand, evaluate and control the epidemic processes.They promoted a dramatic change in how epidemiologists thought of the propagation of infectious diseases.In the last decade, when the traditional epidemiological models seemed to be exhausted, new types of models were developed.These new models incorporated concepts from graph theory to describe and model the underlying social structure.Many of these works merely produced a more detailed extension of the previous results, but some others triggered a completely new paradigm in the mathematical study of epidemic processes. In this review, we will introduce the basic concepts of epidemiology, epidemic modeling and networks, to finally provide a brief description of the most relevant results in the field.
The main aim of the Symposium is to facilitate the meeting of people working on different topics in different fields (mainly Economics, Finance and Computer Science) in order to encourage a structured multi-disciplinary approach to social sciences. Presentations and keynote sessions center around multi-agent modelling, from the viewpoint of both applications and computer-based tools. The event is also open to methodological surveys.
The event will be hosted by Social Simulation 2014, the 10th Conference of the European Social Simulation Association at the Universitat Autonoma de Barcelona, Barcelona, Spain. September 1-5th, 2014.
The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.
In the 1960s Schelling devised a simple model in which a mixed group of people spontaneously segregates by race even though no one in the population desires that outcome. Initially, black and white families are randomly distributed. At each step in the modeling process the families examine their immediate neighborhood and either stay put or move elsewhere depending on whether the local racial composition suits their preferences. The procedure is repeated until everyone finds a satisfactory home (or until the simulator’s patience is exhausted).
Piotr Bródka, Tomasz Filipowski, Przemysław Kazienko (Submitted on 26 Sep 2012) Social communities extraction and their dynamics are one of the most important problems in today's social network analysis. During last few years, many researchers have proposed their own methods for group discovery in social networks. However, almost none of them have noticed that modern social networks are much more complex than few years ago. Due to vast amount of different data about various user activities available in IT systems, it is possible to distinguish the new class of social networks called multi-layered social network. For that reason, the new approach to community detection in the multi-layered social network, which utilizes multi-layered edge clustering coefficient is proposed in the paper.
The concept of social networks, in the age of Twitter and Facebook, seems like a really banal one. Social networks, however, have turned out to be a fertile ground for scientific studies of human interactions by not only social scientists, but also by physicists, from which we gain illuminating insights about ourselves and our societies. For example, why, and how, do we make new friends or establish fresh social ties? In this paper, we show that meaningful answers to these questions can be learned, by bringing concepts and methods from statistical physics to bear in a new analysis of the detailed growth dynamics of two networks associated with two online social-networking sites.
How People Interact in Evolving Online Affiliation Networks
Lazaros K. Gallos, Diego Rybski, Fredrik Liljeros, Shlomo Havlin, and Hernán A. Makse
Cities have long been the world’s economic dynamos, but today the speed and scale of their expansion are unprecedented. Through a combination of consumption and investment in physical capital, growing cities could inject up to $30 trillion a year into the world economy by 2025. Understanding cities and their shifting demographics is critical to reaching urban consumers and to preparing for the challenges that will arise from increasing demand for natural resources (such as water and energy) and for capital to invest in new housing, office buildings, and port capacity.
Report|McKinsey Global Institute Urban world: Cities and the rise of the consuming class June 2012 | by Richard Dobbs, Jaana Remes, James Manyika, Charles Roxburgh, Sven Smit and Fabian Schaer
Alexander V. Mantzaris, Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, Desmond J. Higham (Submitted on 20 Jul 2012) We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of 'learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time progresses. However, the high dimensionality and time-dependent nature of the data makes it difficult to explain which brain regions are driving this distinction. Using network centrality measures that respect the arrow of time, we express the data in an extremely compact form that characterizes the aggregate activity of each brain region in each experiment using a single coefficient, while reproducing information about learning that was discovered using the full data set. This compact summary allows key brain regions contributing to centrality to be visualized and interpreted. We thereby provide a proof of principle for the use of recently proposed dynamic centrality measures on temporal network data in neuroscience.
We introduce a new strategy of navigation in undirected networks, including regular, random and complex networks, that is inspired by L\'evy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time and the average time to reach a node on the network. We found that the long-range navigation using the L\'evy random walk strategy, in comparison with the normal random walk strategy, is more efficient to reduce the time to cover the network. The dynamical effect of using the L\'evy walk strategy is to transform a large-world network into a small world. Our exact results provide a general framework that connects two important fields: L\'evy navigation strategies and dynamics in complex
What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.
Complicity is an open access (free to all readers), peer-reviewed journal that publishes original articles on all aspects of education that are informed by the idea of complexity (in its technical, applied, philosophical, theoretical, or narrative manifestations). The journal strives to serve as a forum for both theoretical and practical contributions and to facilitate the exchange of diverse ideas and points of view related to complexity in education.
Social media platforms, such as Twitter, provide a forum for political communication where politicians broadcast messages and where the general public engages in the discussion of pertinent political issues. The open nature of Twitter, together with its large volume of traffic, makes it a useful resource for new forms of ‘passive’ opinion polling , i.e. automatically monitoring and detecting which key issues the general public is concerned about and inferring their voting intentions. In this paper, we present a number of case studies for the automatic analysis of UK political tweets. We investigate the automated sentiment analysis of tweets from UK Members of Parliament (MPs) towards the main political parties. We then investigate using the volume and sentiment of the tweets from other users as a proxy for their voting intention and compare the results against existing poll data. Finally we conduct automatic identification of the key topics discussed by both the MPs and users on Twitter and compare them with the main political issues identified in traditional opinion polls. We describe our data collection methods, analysis tools and evaluation framework and discuss our results and the factors affecting their accuracy.
Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
Does the availability of instant reference checking and “find more like this” research on the Internet change the standards by which academics should feel “obligated” to cite the work of others? Is the deliberate refusal to look for the existence of parallel work by others an ethical lapse or merely negligence? At a minimum, the Dutch standard of Slodderwetenschap (sloppy science) is clearly at work. At a maximum so is plagiarism. In between sits the process to be labeled as ‘plagiarism by negligence’. This article seeks to expose the intellectual folly of allowing such a plagiarism to be tolerated by the academy through a discussion of the cases of Terrence Deacon and Stephen Wolfram.
Subliminal Influence or Plagiarism by Negligence? The Slodderwetenschap of Ignoring the Internet
The new science of complex systems will be at the heart of the future of the Worldwide Knowledge Society. It is providing radical new ways of understanding the physical, biological, ecological, and techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching (...)
Researchers find the source of an epidemic using relatively little information. Their technique could also help authorities track down contamination in water systems or locate problems in electrical grids.
János Török, Gerardo Iñiguez, Taha Yasseri, Maxi San Miguel, Kimmo Kaski, János Kertész (Submitted on 20 Jul 2012) Information-communication technology promotes collaborative environments like Wikipedia where, however, controversiality and conflicts can appear. To describe the rise, persistence, and resolution of such conflicts we devise an extended opinion dynamics model where agents with different opinions perform a single task to make a consensual product. As a function of the convergence parameter describing the influence of the product on the agents, the model shows spontaneous symmetry breaking of the final consensus opinion represented by the medium. For the case when agents are replaced with new ones at a certain rate, a transition from mainly consensus to a perpetual conflict occurs, which is in qualitative agreement with the scenarios observed in Wikipedia.
Alex Kindler, Sorin Solomon, Dietrich Stauffer (Submitted on 22 Jul 2012) Revolution dynamics is studied through a minimal Ising model with three main influences (fields): personal conservatism (power-law distributed), inter-personal and group pressure, and a global field incorporating peer-to-peer and mass communications, which is generated bottom-up from the revolutionary faction. A rich phase diagram appears separating possible terminal stages of the revolution, characterizing failure phases by the features of the individuals who had joined the revolution. An exhaustive solution of the model is produced, allowing predictions to be made on the revolution's outcome.