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  • 1.
    Jia, Tao
    et al.
    KTH.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Urban and regional planning/GIS-institute.
    Building and analyzing the US airport network based on en-route location information2012In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 391, no 15, p. 4031-4042Article in journal (Refereed)
    Abstract [en]

    From a complex network perspective, this study sets out two aims around the US airport network (USAN) which is built from en-route location information of domestic flights in the US. First, we analyze the structural properties of the USAN with respect to its binary and weighted graphs, and second we explore the airport patterns, which have wide-ranging implications. Results from the two graphs indicate the following. (1) The USAN exhibits scale-free, small-world and disassortative mixing properties, which are consistent with the mainstream perspectives. Besides, we find (2) a remarkable power relationship between the structural measurements in the binary graph and the traffic measurements in the weighted counterpart, namely degree versus capacity and attraction versus volume. On the other hand, investigation of the airport patterns suggests (3) that all the airports can be classified into four categories based on multiple network metrics, which shows a complete typology of the airports. And it further indicates (4) that there is a subtle relationship between the airport traffic and the geographical constraints as well as the regional socioeconomic indicators.

  • 2.
    Jiang, Bin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    A complex-network perspective on Alexander's wholeness2016In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 463, p. 475-484Article in journal (Refereed)
    Abstract [en]

    The wholeness, conceived and developed by Christopher Alexander, is what exists to some degree or other in space and matter, and can be described by precise mathematical language. However, it remains somehow mysterious and elusive, and therefore hard to grasp. This paper develops a complex network perspective on the wholeness to better understand the nature of order or beauty for sustainable design. I bring together a set of complexity-science subjects such as complex networks, fractal geometry, and in particular underlying scaling hierarchy derived by head/tail breaks — a classification scheme and a visualization tool for data with a heavy-tailed distribution, in order to make Alexander’s profound thoughts more accessible to design practitioners and complexity-science researchers. Through several case studies (some of which Alexander studied), I demonstrate that the complex-network perspective helps reduce the mystery of wholeness and brings new insights to Alexander’s thoughts on the concept of wholeness or objective beauty that exists in fine and deep structure. The complex-network perspective enables us to see things in their wholeness, and to better understand how the kind of structural beauty emerges from local actions guided by the 15 fundamental properties, and in particular by differentiation and adaptation processes. The wholeness goes beyond current complex network theory towards design or creation of living structures.

  • 3.
    Jiang, Bin
    Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
    A topological pattern of urban street networks: universality and peculiarity2007In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 384, no 2, p. 647-655Article in journal (Refereed)
    Abstract [en]

    In this paper, we derive a topological pattern of urban street networks using a large sample (the largest so far to the best of our knowledge) of 40 US cities and a few more from elsewhere of different sizes. It is found that all the topologies of urban street networks based on street-street intersection demonstrate a small world structure, and a scale-free property for both street length and connectivity degree. More specifically, for any street network, about 80% of its streets have length or degrees less than its average value, while 20% of streets have length or degrees greater than the average. Out of the 20%, there are less than 1 % of streets which can form a backbone of the street network. Based on the finding, we conjecture that the 20% streets account for 80% of traffic flow, and the I% streets constitute a cognitive map of the urban street network. We illustrate further a peculiarity about the scale-free property.

  • 4.
    Jiang, Bin
    Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
    Flow dimension and capacity for structuring urban street networks2008In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 387, no 16-17, p. 4440-4452Article in journal (Refereed)
    Abstract [en]

    This paper aims to measure the efficiency of urban street networks (a kind of complex networks) from the perspective of themultidimensional chain of connectivity (or flow). More specifically, we define two quantities: flow dimension and flow capacity, tocharacterize structures of urban street networks. To our surprise, for the topologies of urban street networks, previously confirmedas a form of small world and scale-free networks, we find that (1) the range of their flow dimensions is rather wider than theirrandom and regular counterparts, (2) their flow dimension shows a power-law distribution, and (3) they have a higher flow capacitythan their random and regular counterparts. The findings confirm that (1) both the wider range of flow dimensions and the higherflow capacity can be a signature of small world networks, and (2) the flow capacity can be an alternative quantity for measuring theefficiency of networks or that of the individual nodes. The findings are illustrated using three urban street networks (two in Europeand one in the USA).

  • 5.
    Jiang, Bin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Ma, Ding
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
    Defining least community as a homogeneous group in complex networks2015In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 428, p. 154-160Article in journal (Refereed)
    Abstract [en]

    This paper introduces a new concept of least community that is as homogeneous as a random graph, and develops a new community detection algorithm from the perspective of homogeneity or heterogeneity. Based on this concept, we adopt head/tail breaks-a newly developed classification scheme for data with a heavy-tailed distribution-and rely on edge betweenness given its heavy-tailed distribution to iteratively partition a network into many heterogeneous and homogeneous communities. Surprisingly, the derived communities for any self-organized and/or self-evolved large networks demonstrate very striking power laws, implying that there are far more small communities than large ones. This notion of far more small things than large ones constitutes a new fundamental way of thinking for community detection. © 2015 Elsevier B.V. All rights reserved.

  • 6.
    Stratimirovic, Dj.
    et al.
    Faculty of Stomatology, Department of Physics, University of Belgrade, Belgrade, Serbia and Montenegro.
    Milosevic, S.
    Faculty of Physics, University of Belgrade, Belgrade, Serbia and Montenegro.
    Blesic, S.
    Institute for Medical Research, Laboratory for Neurophysiology, Belgrade, Serbia and Montenegro.
    Ljubisavljevic, M.
    University of Gävle, Centre for Musculoskeletal Research.
    Wavelet transform analysis of time series generated by the stimulated neuronal activity2007In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 374, no 2, p. 699-706Article in journal (Refereed)
    Abstract [en]

    We have studied the stimulated discharge dynamics of fusimotor neurons by applying the wavelet transform technique and by adopting that the neuronal discharge dynamics is manifested by the random time series of interspike intervals. We found two different power-law type behaviors along interspike intervals (ISI) time scale (which implies existence of two different types of neuronal noise), which are separated by a crossover region. Our results reveal that complex neuronal dynamics, in the presence of external stimulation, is manifested with long-range correlated noise in the region before the crossover, on the ISI time scale.

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