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A Novel Procedure for Measuring Semantic Synergy

One interesting characteristic of some complex systems is the formation of macro level constructions perceived as having features that cannot be reduced to their micro level constituents. This characteristic is considered to be the expression of synergy where the joint action of the constituents produces unique features that are irreducible to the constituents isolated behavior or their simple composition. The synergy, characterizing complex systems, has been well acknowledged but difficult to conceptualize and quantify in the context of computing the emerging meaning of various linguistic and conceptual constructs. In this paper, we propose a novel measure/procedure for quantifying semantic synergy. This measure draws on a general idea of synergy as has been proposed in biology. We validate this measure by providing evidence for its ability to predict the semantic transparency of linguistic compounds (Experiment 1) and the abstractness rating of nouns (Experiment 2).

 

A Novel Procedure for Measuring Semantic Synergy
Yair Neuman, Yiftach Neuman, and Yochai Cohen

Complexity
Volume 2017 (2017), Article ID 5785617, 8 pages
https://doi.org/10.1155/2017/5785617


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Explaining the prevalence, scaling and variance of urban phenomena

Explaining the prevalence, scaling and variance of urban phenomena | Network Science | Scoop.it

The prevalence of many urban phenomena changes systematically with population size 1 . We propose a theory that unifies models of economic complexity 2,3 and cultural evolution 4 to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.

 

Explaining the prevalence, scaling and variance of urban phenomena
Andres Gomez-Lievano, Oscar Patterson-Lomba & Ricardo Hausmann

Nature Human Behaviour 1, Article number: 0012 (2016)
doi:10.1038/s41562-016-0012


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Mason A. Porter: Cascades and Social influence on networks

I discuss "simple" dynamical systems on networks and examine how network structure affects dynamics of processes running on top of networks. I'll give an introduction to the idea of social ("complex") contagions, and I'll present a model for multi-stage complex contagions in which fanatics produce greater influence than mere followers.  I'll also briefly discuss the use of ideas from topics like persistent homology to examine wavefront propagation versus the appearance of new contagion clusters, and I'll present a model (without network structure) for the adoption of applications on Facebook. The last family of models illustrates how very different time-dependent dynamics can produce quantitatively similar long-time behavior, which poses both very serious challenges and exciting opportunities for the modeling of complex systems.


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Reputation and impact in academic careers

Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation has much less influence relative to the paper’s citation count. In science, perceived quality, and decisions made based on those perceptions, is increasingly linked to citation counts. Shedding light on the complex mechanisms driving these quantitative measures facilitates not only better evaluation of scientific outputs but also a more transparent evaluation of the scientists producing them.

 

Reputation and impact in academic careers
Alexander Michael Petersen, Santo Fortunato, Raj K. Pan, Kimmo Kaski, Orion Penner, Armando Rungi, Massimo Riccaboni, H. Eugene Stanley, and Fabio Pammolli

PNAS

http://dx.doi.org/10.1073/pnas.1323111111


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Towards a Methodology for Validation of Centrality Measures in Complex Networks

Towards a Methodology for Validation of Centrality Measures in Complex Networks | Network Science | Scoop.it

Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

 

Batool K, Niazi MA (2014) Towards a Methodology for Validation of Centrality Measures in Complex Networks. PLoS ONE 9(4): e90283. http://dx.doi.org/10.1371/journal.pone.0090283


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Liz Rykert's curator insight, April 15, 2014 10:50 PM

Love this stuff.

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Geo-located Twitter as proxy for global mobility patterns

Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow–outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns.

 

Geo-located Twitter as proxy for global mobility patterns

Bartosz Hawelka*, Izabela Sitko, Euro Beinat, Stanislav Sobolevsky, Pavlos Kazakopoulos & Carlo Ratti

Cartography and Geographic Information Science

http://dx.doi.org/10.1080/15230406.2014.890072 ;


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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic–mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.

 

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Dirk Brockmann, Dirk Helbing

Science 13 December 2013:
Vol. 342 no. 6164 pp. 1337-1342
http://dx.doi.org/10.1126/science.1245200


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Phillip Trotter's curator insight, December 31, 2013 3:59 AM

This is an awesome insight that needs tested across other datasets to find out how universal it is. Good paper.

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Significant Scales in Community Structure

Significant Scales in Community Structure | Network Science | Scoop.it

Many complex networks show signs of modular structure, uncovered by community detection. Although many methods succeed in revealing various partitions, it remains difficult to detect at what scale some partition is significant. This problem shows foremost in multi-resolution methods. We here introduce an efficient method for scanning for resolutions in one such method. Additionally, we introduce the notion of “significance” of a partition, based on subgraph probabilities. Significance is independent of the exact method used, so could also be applied in other methods, and can be interpreted as the gain in encoding a graph by making use of a partition. Using significance, we can determine “good” resolution parameters, which we demonstrate on benchmark networks. Moreover, optimizing significance itself also shows excellent performance. We demonstrate our method on voting data from the European Parliament. Our analysis suggests the European Parliament has become increasingly ideologically divided and that nationality plays no role.

 

Significant Scales in Community Structure
V. A. Traag, G. Krings & P. Van Dooren

Scientific Reports 3, Article number: 2930 http://dx.doi.org/10.1038/srep02930


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Detection of timescales in evolving complex systems

Detection of timescales in evolving complex systems | Network Science | Scoop.it

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system’s configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.

 

Detection of timescales in evolving complex systems
Richard K. Darst, Clara Granell, Alex Arenas, Sergio Gómez, Jari Saramäki & Santo Fortunato

Scientific Reports 6, Article number: 39713 (2016)
doi:10.1038/srep39713


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Identifying the structural discontinuities of human interactions

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. In the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls and uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylize the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models, thus increasing our ability to predict social activities and to plan the development of infrastructures across multiple scales.

 

Identifying the structural discontinuities of human interactions
Sebastian Grauwin, Michael Szell, Stanislav Sobolevsky, Philipp Hövel, Filippo Simini, Maarten Vanhoof, Zbigniew Smoreda, Albert-Laszlo Barabasi, Carlo Ratti

http://arxiv.org/abs/1509.03149

 


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Spatial patterns of close relationships across the lifespan

The dynamics of close relationships is important for understanding the migration patterns of individual life-courses. The bottom-up approach to this subject by social scientists has been limited by sample size, while the more recent top-down approach using large-scale datasets suffers from a lack of detail about the human individuals. We incorporate the geographic and demographic information of millions of mobile phone users with their communication patterns to study the dynamics of close relationships and its effect in their life-course migration. We demonstrate how the close age- and sex-biased dyadic relationships are correlated with the geographic proximity of the pair of individuals, e.g., young couples tend to live further from each other than old couples. In addition, we find that emotionally closer pairs are living geographically closer to each other. These findings imply that the life-course framework is crucial for understanding the complex dynamics of close relationships and their effect on the migration patterns of human individuals.


Spatial patterns of close relationships across the lifespan
• Hang-Hyun Jo, Jari Saramäki, Robin I. M. Dunbar & Kimmo Kaski

Scientific Reports 4, Article number: 6988 http://dx.doi.org/10.1038/srep06988


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The Rise of Social Bots

The Turing test asked whether one could recognize the behavior of a human from that of a computer algorithm. Today this question has suddenly become very relevant in the context of social media, where text constraints limit the expressive power of humans, and real incentives abound to develop human-mimicking software agents called social bots. These elusive entities wildly populate social media ecosystems, often going unnoticed among the population of real people. Bots can be benign or harmful, aiming at persuading, smearing, or deceiving. Here we discuss the characteristics of modern, sophisticated social bots, and how their presence can endanger online ecosystems and our society. We then discuss current efforts aimed at detection of social bots in Twitter. Characteristics related to content, network, sentiment, and temporal patterns of activity are imitated by bots but at the same time can help discriminate synthetic behaviors from human ones, yielding signatures of engineered social tampering.

 

The Rise of Social Bots
Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, Alessandro Flammini

http://arxiv.org/abs/1407.5225


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The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group

The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group | Network Science | Scoop.it
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.

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Information Evolution in Social Networks

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

 

Information Evolution in Social Networks
Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng

http://arxiv.org/abs/1402.6792


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António F Fonseca's curator insight, March 1, 2014 2:00 PM

Memes are the information science counterpath of particles to physics.

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Universality in network dynamics

Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system’s response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system’s dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system’s dynamics.

 

Universality in network dynamics
Baruch Barzel & Albert-László Barabási

Nature Physics 9, 673–681 (2013) http://dx.doi.org/10.1038/nphys2741


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Complexity Digest - Networking the Complexity Community

Complexity Digest - Networking the Complexity Community | Network Science | Scoop.it
Complexity Digest provides regular digests from papers related to the scientific study of complex systems.

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