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Effects of degree-biased transmission rate and nonlinear infectivity on rumor spreading in complex social networks

Effects of degree-biased transmission rate and nonlinear infectivity on rumor spreading in complex social networks | Network Science | Scoop.it

We introduce a generalized rumor spreading model and analytically investigate the spreading of rumors on scale-free (SF) networks.

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Network Science
Network Science explores the powerful relations in complex systems
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Homological scaffolds of brain functional networks | TOPDRIM

Homological scaffolds of brain functional networks | TOPDRIM | Network Science | Scoop.it

Abstract:

Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186–198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects—homological cycles—associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting- state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mush- rooms. The results show that the homological structure of the brain’s functional patterns undergoes a dramatic change post-psilocybin, characterised by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.

David Rodrigues's insight:

How magic mushrooms play tricks in your brain! 

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Topology: The Secret Ingredient In The Latest Theory of Everything

Topology: The Secret Ingredient In The Latest Theory of Everything | Network Science | Scoop.it

Combine topology with symmetry and add a sprinkling of quantum mechanics. The result? A powerful new theory of everything


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Networks thrive in complexity

Networks thrive in complexity | Network Science | Scoop.it

In complex environments, weak hierarchies and strong networks are the best organizing principle. One good example of complexity that we can try to fathom is nature itself. Networks thrive in nature. As Howard Bloom stated in a speech at Yale University


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Tim Williamson's curator insight, December 30, 2012 1:39 PM

Excellent work.  I would add that to properly understand complex systems on the global scale requires a holistic approach.  You can somewhat understand the mechanics of the system only generally since these systems are emergent by definition, but to 'influence' the outcomes of an emergent system requires a holistic understanding.

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On the Bursty Evolution of Online Social Networks

by Sabrina Gaito, Matteo Zignani, Gian Paolo Rossi, Alessandra Sala, Xiao Wang, Haitao Zheng, Ben Y. Zhao

 

The high level of dynamics in today's online social networks (OSNs) creates new challenges for their infrastructures and providers. In particular, dynamics involving edge creation has direct implications on strategies for resource allocation, data partitioning and replication. Understanding network dynamics in the context of physical time is a critical first step towards a predictive approach towards infrastructure management in OSNs. Despite increasing efforts to study social network dynamics, current analyses mainly focus on change over time of static metrics computed on snapshots of social graphs. The limited prior work models network dynamics with respect to a logical clock. In this paper, we present results of analyzing a large timestamped dataset describing the initial growth and evolution of Renren, the leading social network in China. We analyze and model the burstiness of link creation process, using the second derivative, i.e. the acceleration of the degree. This allows us to detect bursts, and to characterize the social activity of a OSN user as one of four phases: acceleration at the beginning of an activity burst, where link creation rate is increasing; deceleration when burst is ending and link creation process is slowing; cruising, when node activity is in a steady state, and complete inactivity.

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The Geometric Structure of the Brain Fiber Pathways

by Van J. Wedeen, Douglas L. Rosene, Ruopeng Wang, Guangping Dai, Farzad Mortazavi, Patric Hagmann, Jon H. Kaas, Wen-Yih I. Tseng

 

The structure of the brain as a product of morphogenesis is difficult to reconcile with the observed complexity of cerebral connectivity. We therefore analyzed relationships of adjacency and crossing between cerebral fiber pathways in four nonhuman primate species and in humans by using diffusion magnetic resonance imaging. The cerebral fiber pathways formed a rectilinear three-dimensional grid continuous with the three principal axes of development. Cortico-cortical pathways formed parallel sheets of interwoven paths in the longitudinal and medio-lateral axes, in which major pathways were local condensations. Cross-species homology was strong and showed emergence of complex gyral connectivity by continuous elaboration of this grid structure. This architecture naturally supports functional spatio-temporal coherence, developmental path-finding, and incremental rewiring with correlated adaptation of structure and function in cerebral plasticity and evolution.

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James Fowler: Power of Networks | PopTech! Popcasts

James Fowler: Power of Networks | PopTech! Popcasts | Network Science | Scoop.it

Can your social network make you fat? Affect your mood? Political scientist James H. Fowler reveals the dynamics of social networks, the invisible webs that connect each of us to the other. With Nicholas A Christakis, Fowler recently coauthored, Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives..


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Network Structure, Topology and Dynamics in Generalized Models of Synchronization

by Kristina Lerman, Rumi Ghosh


"We explore the interplay of network structure, topology, and dynamic interactions between nodes using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, interconnected oscillators synchronize in stages, revealing network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process, such as diffusion. However, social and biological processes are often non-conservative. We propose a new model of synchronization in a network of oscillators coupled via non-conservative processes. We study dynamics of synchronization of a synthetic and real-world networks and show that different synchronization models reveal different structures within the same network."

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[1203.4942] Analytical investigation of self-organized criticality in neural networks

Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.

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Networks, smart government and the Overton window | Synthesis

A fundamental feature of 21st century society is the way in which networks shape and determine outcomes. This implies a radical shift in the conduct of public policy. It does not mean ‘no government’. But it means ‘smart government’.

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How Networks Changed the "Scale" of Our World

Collective Dynamics of Complex Systems Research Group Seminar Series February 22, 2012 Hiroki Sayama (Bioengineering & Systems Science and Industrial Engineering, Binghamton University) "How Networks Changed the "Scale" of Our World"...

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Quasiperiodic graphs: structural design, scaling and entropic properties

A novel class of graphs, here named quasiperiodic, are constructed via application of the Horizontal Visibility algorithm to the time series generated along the quasiperiodic route to chaos. We show how the hierarchy of mode-locked regions represented by the Farey tree is inherited by their associated graphs. We are able to establish, via Renormalization Group (RG) theory, the architecture of the quasiperiodic graphs produced by irrational winding numbers with pure periodic continued fraction. And finally, we demonstrate that the RG fixed-point degree distributions are recovered via optimization of a suitably defined graph entropy.

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Data structure for Social news streams on Graph data bases

Data structure for Social news streams on Graph data bases | Network Science | Scoop.it
Data structure for Social news streams on Graph data bases http://t.co/qI0EJUmo...
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ECCS'14: Satellite Meeting Information Processing in Complex Systems (IPCS'14)

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. 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 and transfer 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.

 

Satellite meeting: September 24, 2014 at ECCS'14, Lucca, Italy.

 

Program and speakers listed at the satellite website.

http://www.computationalscience.nl/ipcs2014/


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Information Processing in Complex Networks | TOPDRIM

David Rodrigues's insight:

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. 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 and transfer 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.

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Long-Range Navigation on Complex Networks using Lévy Random Walks

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

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Explosive first-order transition to synchrony in networked chaotic oscillators

I. Leyva, R. Sevilla-Escoboza, J. M. Buldú, I. Sendiña-Nadal, J. Gómez-Gardeñes, A. Arenas, Y. Moreno, S. Gómez, R.Jaimes-Reátegui, S. Boccaletti

 

Critical phenomena in complex networks, and the emergence of dynamical abrupt transitions in the macroscopic state of the system are currently a subject of the outmost interest. We report evidence of an explosive phase synchronization in networks of chaotic units. Namely, by means of both extensive simulations of networks made up of chaotic units, and validation with an experiment of electronic circuits in a star configuration, we demonstrate the existence of a first order transition towards synchronization of the phases of the networked units. Our findings constitute the first prove of this kind of synchronization in practice, thus opening the path to its use in real-world applications.

 

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Consensus clustering in complex networks

Consensus clustering in complex networks | Network Science | Scoop.it

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods.


Via Complexity Digest, Spaceweaver
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CSNA 2012 - International Workshop on Complex Social Network Analysis

CSNA 2012 - International Workshop on Complex Social Network Analysis | Network Science | Scoop.it

The International Workshop on Complex Social Network Analysis (CSNA 2012) is held as a part of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), 26-29 August, 2012, Kadir Has University, Istanbul, Turkey.

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Exploring complex networks by means of adaptive walkers

by Luce Prignano, Yamir Moreno, Albert Diaz-Guilera

"Finding efficient algorithms to explore large networks with the aim of recovering information about their structure is an open problem. Here, we investigate this challenge by proposing a model in which random walkers with previously assigned home nodes navigate through the network during a fixed amount of time. We consider that the exploration is successful if the walker gets the information gathered back home, otherwise, no data is retrieved. Consequently, at each time step, the walkers, with some probability, have the choice to either go backward approaching their home or go farther away. We show that there is an optimal solution to this problem in terms of the average information retrieved and the degree of the home nodes and design an adaptive strategy based on the behavior of the random walker. Finally, we compare different strategies that emerge from the model in the context of network reconstruction. Our results could be useful for the discovery of unknown connections in large scale networks."

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Effects of degree-biased transmission rate and nonlinear infectivity on rumor spreading in complex social networks

Effects of degree-biased transmission rate and nonlinear infectivity on rumor spreading in complex social networks | Network Science | Scoop.it

We introduce a generalized rumor spreading model and analytically investigate the spreading of rumors on scale-free (SF) networks.

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[1203.4916] Cluster and group synchronization in delay-coupled networks

We investigate the stability of synchronized states in delay-coupled networks where synchronization takes place in groups of different local dynamics or in cluster states in networks with identical local dynamics. Using a master stability approach, we find that the master stability function shows a discrete rotational symmetry depending on the number of groups. The coupling matrices that permit solutions on group or cluster synchronization manifolds show a very similar symmetry in their eigenvalue spectrum, which helps to simplify the evaluation of the master stability function. Our theory allows for the characterization of stability of different patterns of synchronized dynamics in networks with multiple delay times, multiple coupling functions, but also with multiple kinds of local dynamics in the networks' nodes. We illustrate our results by calculating stability in the example of delay-coupled semiconductor lasers and in a model for neuronal spiking dynamics.

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PLoS ONE: Evolution of Opinions on Social Networks in the Presence of Competing Committed Groups

PLoS ONE: Evolution of Opinions on Social Networks in the Presence of Competing Committed Groups | Network Science | Scoop.it

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network.

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Duncan Watts - Social contagion: What do we really know?

Duncan Watts - Social contagion: What do we really know? | Network Science | Scoop.it
PopTech is a global community of innovators, working together to expand the edge of change.
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So What The Heck Is The 'Social Graph' Facebook Keeps Talking About?

So What The Heck Is The 'Social Graph' Facebook Keeps Talking About? | Network Science | Scoop.it
It's pretty crazy to think that Mark Zuckerberg created a social network site in his dormroom in college and managed to grow it into a $100 billion company. Zuckerberg is a genius. He helped the...
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