In this work we study a peculiar example of social organization on Facebook: the Occupy Movement -- i.e., an international protest movement against social and economic inequality organized online at a city level. We consider 179 US Facebook public pages during the time period between September 2011 and February 2013. The dataset includes 618K active users and 753K posts that received about 5.2M likes and 1.1M comments. By labeling user according to their interaction patterns on pages -- e.g., a user is considered to be polarized if she has at least the 95% of her likes on a specific page -- we find that activities are not locally coordinated by geographically close pages, but are driven by pages linked to major US cities that act as hubs within the various groups. Such a pattern is verified even by extracting the backbone structure -- i.e., filtering statistically relevant weight heterogeneities -- for both the pages-reshares and the pages-common users networks.
Structural Patterns of the Occupy Movement on Facebook Michela Del Vicario, Qian Zhang, Alessandro Bessi, Fabiana Zollo, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi
Here you will find a description of the aims, activities and theoretical synthesis of the "Information - Function - Biology" project.
The broad aim is to advance a deep understanding of life and living which integrates concepts over all scales, all time and all forms of biological organisation. The key insight enabling this is to see that living is an information process, that living forms are concentrations of information engaged in storing, communicating, filtering and recombining information. There are several inspirations for this work, but perhaps the most prominent is the book by Erwin Schrodinger, which he called “What is Life?*”. That is why this website has the URL http://www.whatlifeis.info
There are approximately 3 billion people in the global workforce. LinkedIn's vision is to create economic opportunity for every one of them. The development of the world's first Economic Graph will lead to making that vision a reality. This, of course, is no easy task. Our vision is grand, but it's not unattainable.
So, here's the challenge: Given the wealth of data that exists within LinkedIn, what research would you propose that has the potential to create greater economic opportunity?
We are launching the LinkedIn Economic Graph Challenge to encourage researchers, academics and data-driven thinkers to solve some of the most challenging economic problems of our times.
The New England Complex Systems Institute has funding for postdoctoral and predoctoral research appointments. We look for outstanding applicants with training in physics, mathematics or computer science. We value strong writing abilities. Candidates should be interested in contributing to a wide range of NECSI's research areas, including analysis and modeling of
Socio-economic systems relevant to: - The food and economic crises, - Conflicts, revolutions, and ethnic violence - International development, and - Pandemics
What are the neural signatures of consciousness? This is an elusive yet fascinating challenge to current cognitive neuroscience, but it takes on an immediate clinical and societal significance in patients diagnosed as vegetative and minimally conscious. In these patients, it leads us to ask whether we can test for the presence of these signatures in the absence of any external signs of awareness. Recent conceptual advances suggest that consciousness requires a dynamic balance between integrated and differentiated networks of information exchange between brain regions. Here we apply this insight to study such networks in patients and compare them to healthy adults. Using the science of graph theory, we show that the rich and diversely connected networks that support awareness are characteristically impaired in patients, lacking the ability to efficiently integrate information across disparate regions via well-connected hubs. We find that the quality of patients' networks also correlates well with their degree of behavioural responsiveness, and some vegetative patients who show signs of hidden awareness have remarkably well-preserved networks similar to healthy adults.
The Western Ghats in India rise like a wall between the Arabian Sea and the heart of the subcontinent to the east. The 1,000-mile-long chain of coastal mountains is dense with lush rainforest and grasslands, and each year, clouds bearing monsoon rains blow in from the southwest and break against the mountains’ flanks, unloading water…
Complex Adaptive Systems Modeling welcomes submissions to the new thematic series on Modeling large-scale communication networks using complex networks and agent-based modeling techniques. This thematic series intends to publish high quality original research as well as review articles on case studies, models and methods for the modeling and simulation of large-scale computer communication networks using either of the following two approaches:
Complex networks (such as modeled using tools such as Gephi, Network Workbench and others) Agent-based models (such as based on NetLogo, Repast, Mason, Swarm and others)
Recently much attention has been paid to the study of the robustness of interdependent and multiplex networks and, in particular, the networks of networks. The robustness of interdependent networks can be evaluated by the size of a mutually connected component when a fraction of nodes have been removed from these networks. Here we characterize the emergence of the mutually connected component in a network of networks in which every node of a network (layer) alpha is connected with q_alpha its randomly chosen replicas in some other networks and is interdependent of these nodes with probability r. We find that when the superdegrees q_alpha of different layers in a network of networks are distributed heterogeneously, multiple percolation phase transition can occur. We show that, depending on the value of r, these transition are continuous or discontinuous.
Transfer entropy is a recently introduced information-theoretic measure quantifying directed statistical coherence between spatiotemporal processes, and is widely used in diverse fields ranging from finance to neuroscience. However, its relationships to fundamental limits of computation, such as Landauer's limit, remain unknown. Here we show that in order to increase transfer entropy (predictability) by one bit, heat flow must match or exceed Landauer's limit. Importantly, we generalise Landauer's limit to bi-directional information dynamics for non-equilibrium processes, revealing that the limit applies to prediction, in addition to retrodiction (information erasure). Furthermore, the results are related to negentropy, and to Bremermann's limit and the Bekenstein bound, producing, perhaps surprisingly, lower bounds on the computational deceleration and information loss incurred during an increase in predictability about the process. The identified relationships set new computational limits in terms of fundamental physical quantities, and establish transfer entropy as a central measure connecting information theory, thermodynamics and theory of computation.
The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent.
The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org ) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.
A draft map of the human proteome • Min-Sik Kim, et al.
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.
All systems in nature have one thing in common: they process information. Information is registered in the state of a system and its elements, implicitly and invisibly. As elements interact, information is transferred and modified. Indeed, bits of information about the state of one element will travel—imperfectly—to the state of the other element, forming its new state. This storage, transfer, and modification of information, possibly between levels of a multi level system, is imperfect due to randomness or noise. From this viewpoint, a system can be formalized as a collection of bits that is organized according to its rules of dynamics and its topology of interactions. Mapping out exactly how these bits of information percolate through the system could reveal new fundamental insights in how the parts orchestrate to produce the properties of the system. A theory of information processing would be capable of defining a set of universal properties of dynamical multi level complex systems, which describe and compare the dynamics of diverse complex systems ranging from social interaction to brain networks, from financial markets to biomedicine. Each possible combination of rules of dynamics and topology of interactions, with disparate semantics, would reduce to a single language of information processing.
Guest Editor: Dr. Rick Quax
Deadline for manuscript submissions: 28 February 2015
Complexity, Governance, and Networks aims to contribute to the philosophical, theoretical, methodological, and empirical developments in complexity, governance, and network studies in public administration, public policy, politics, and non-governmental organizations. The journal publishes primarily theoretical essays and original research papers.
The Complex Systems Summer School offers an intensive four week introduction to complex behavior in mathematical, physical, living, and social systems for graduate students and postdoctoral fellows in the sciences and social sciences. The school is for participants who seek background and hands-on experience to help them prepare to conduct interdisciplinary research in areas related to complex systems.
Funding is available for applicants interested in carrying out fundamental and applied research in the field of complex systems. The research will involve theoretical work as well as computer simulations. It will aim to discover fundamental connections between information-theoretic and statistical-mechanical approaches to self-organisation, while investigating a variety of topics in nonlinear critical phenomena, with particular focus on information dynamics during phase transitions.
The PhD will be supervised by Prof. Mikhail Prokopenko. The applicant will join the Complex Systems Research Group (CSRG) at The School of Civil Engineering – The University of Sydney. The CSRG group comprises ten academics, and has wide collaborations across the University, Australia, and internationally. It is a vibrant, world-leading group in the fields of guided self-organisation and critical phenomena forecasting.
The scholarship also includes covering the fees payable by international students.
Following last years successful edition, we have once more decided to organize a summer school coinciding with the European Conference on Complex Systems thus profiting the opportunity offered by the presence of a wide variety of experts in different topics in Lucca. The projected school aims to offer young researchers the opportunity to learn new methods, present their work and meet fellow researchers, and it also represents a good opportunity for young researcher to prepare their participation to the main ECCS conference in an informal and relaxed environment. Following our policy to display local talent, three renowned italian researchers will each present a different aspect of complex networks in three hour sessions. Names such as Dr. Roberta Sinatra, Dr. Ciro Catutto and Prof. Stefano Battiston should sound familiar to any interested student. Furthermore, we plan a meeting where each participant will have the possibility to share with the others his work, organized as a flash presentation workshop. Of course, a major social event is also included, to stimulate networking and “prepare” the official ECCS conference.
Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.
Over the course of human history, thousands of languages have developed from what was once a much smaller number. How did we end up with so many? And how do we keep track of them all? Alex Gendler explains how linguists group languages into language families, demonstrating how these linguistic trees give us crucial insights into the past.
Never before were politicians, business leaders, and scientists more urgently needed to master the challenges ahead of us. We are in the middle of a third industrial revolution. While we see the symptoms, such as the financial and economic crisis, cybercrime and cyberwar, we haven't understood the implications well. But at the end of this socio-economic transformation, we will live in a digital society. This comes with breath-taking opportunities and challenges, as they occur only every 100 years.
Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be. DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702
Origin of Peer Influence in Social Networks Phys. Rev. Lett. 112, 098702 – Published 6 March 2014 Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco
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