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  • Journal article
    Font-Clos F, Pruessner G, Moloney NR, Deluca Aet al., 2015,

    The perils of thresholding

    , New Journal of Physics, Vol: 17, ISSN: 1367-2630

    The thresholding of time series of activity or intensity is frequently used to define and differentiateevents. This is either implicit, for example due to resolution limits, or explicit, in order to filter certainsmall scale physics from the supposed true asymptotic events. Thresholding the birth–death process,however, introduces a scaling region into the event size distribution, which is characterized by anexponent that is unrelated to the actual asymptote and is rather an artefact of thresholding. As a result,numerical fits of simulation data produce a range of exponents, with the true asymptote visible only inthe tail of the distribution. This tail is increasingly difficult to sample as the threshold is increased. Inthe present case, the exponents and the spurious nature of the scaling region can be determinedanalytically, thus demonstrating the way in which thresholding conceals the true asymptote. Theanalysis also suggests a procedure for detecting the influence of the threshold by means of a datacollapse involving the threshold-imposed scale.

  • Journal article
    Kawamoto H, Takayasu H, Jensen HJ, Takayasu Met al., 2015,

    Precise calculation of a bond percolation transition and survival rates of nodes in a complex network

    , PLOS One, Vol: 10, ISSN: 1932-6203

    Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition.

  • Journal article
    Loe CW, Jensen HJ, 2015,

    Comparison of communities detection algorithms for multiplex

    , Physica A - Statistical Mechanics and Its Applications, Vol: 431, Pages: 29-45, ISSN: 0378-4371

    Multiplex is a set of graphs on the same vertex set, i.e. {G(V,E1),…,G(V,Em)}{G(V,E1),…,G(V,Em)}. It is a type of generalized graph to model the multiple relationships in a system with parallel edges between vertices. An important application in Network Science is to capture community structures in multiplex as a way to modularize the system. This paper is a literature review and comparative analysis on the existing communities detection algorithms for multiplex. The conclusion is that many of the algorithms deviate in the concept of multi-relational communities and the wrong choice of algorithm can deviate one from his intended concept.

  • Journal article
    Massobrio P, de Arcangelis L, Pasquale V, Jensen HJ, Plenz Det al., 2015,

    Criticality as a signature of healthy neural systems

    , Frontiers in Systems Neuroscience, Vol: 9, ISSN: 1662-5137
  • Conference paper
    Piovani D, Grujic J, Jensen HJ, 2015,

    Forecasting systemic transitions in high dimensional stochastic complex systems

    , 4th International Conference on Mathematical Modeling in Physical Sciences (IC-MSquare), Publisher: IOP PUBLISHING LTD, ISSN: 1742-6588
  • Conference paper
    Goldberg SR, Anthony H, Evans TS, 2015,

    Do We Need Global and Local Knowledge of the Citation Network?

    , 15th International Conference of the International-Society-for-Scientometrics-and-Informetrics (ISSI) on Scientometrics and Informetrics, Publisher: INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI, Pages: 282-283, ISSN: 2175-1935
  • Conference paper
    Clough JR, Evans TS, 2015,

    Time & Citation Networks

    , 15th International Conference of the International-Society-for-Scientometrics-and-Informetrics (ISSI) on Scientometrics and Informetrics, Publisher: INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI, Pages: 1073-1078, ISSN: 2175-1935
  • Conference paper
    Loach TV, Evans TS, 2015,

    Ranking Journals Using Altmetrics

    , 15th International Conference of the International-Society-for-Scientometrics-and-Informetrics (ISSI) on Scientometrics and Informetrics, Publisher: INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI, Pages: 89-94, ISSN: 2175-1935
  • Journal article
    Jensen HJ, Wan X, Crüts B, 2014,

    The Causal Inference of Cortical Neural Networks during Music Improvisations

    , PLOS One, Vol: 9, ISSN: 1932-6203

    We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and “let-go” mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and “let-go” mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

  • Journal article
    Gastner MT, Markou N, Pruessner G, Draief Met al., 2014,

    Opinion Formation Models on a Gradient

    , PLOS One, Vol: 9, ISSN: 1932-6203

    Statistical physicists have become interested in models of collective social behaviorsuch as opinion formation, where individuals change their inherently preferredopinion if their friends disagree. Real preferences often depend on regional culturaldifferences, which we model here as a spatial gradient g in the initial opinion. Thegradient does not only add reality to the model. It can also reveal that opinionclusters in two dimensions are typically in the standard (i.e., independent)percolation universality class, thus settling a recent controversy about a nonconsensusmodel. However, using analytical and numerical tools, we also present amodel where the width of the transition between opinions scales !g{1=4, not!g{4=7 as in independent percolation, and the cluster size distribution isconsistent with first-order percolation.

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