Number of items: **5**.

## L

Liu, Jianguo,

Dang, Yan-Zhong and

Wang, Zhong-Tuo
(2006)

*Complex networks properties of Chinese natural science basic research.*
Physica A: Statistical Mechanics and its Applications, 366 (2).
pp. 578-586.
Link to full text available through this repository.

- Abstract
In this paper, we studied the research areas of Chinese natural science basic research from a point view of complex network. Two research areas are considered to be connected if they appear in one fund proposal. The explicit network of such connections using data from 1999 to 2004 is constructed. The analysis of the real data shows that the degree distribution of the bf research areas network (RAN) may be better fitted by the exponential distribution. It displays small world effect in which randomly chosen pairs of research areas are typically separated by only a short path of intermediate research areas. The average distance of RAN decreases with time, while the average clustering coefficient increases with time, which indicates that the scientific study would like to be integrated together in terms of the studied areas. The relationship between the clustering coefficient C(k) and the degree k indicates that there is no hierarchical organization in RAN.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Chinese natural science basic research; Clustering coefficient; Complex networks; Evolution network; Power law distribution
- Centre
- CABDyN Complexity Centre

Liu, Jianguo,

Dang, Yan-Zhong,

Wang, Zhong-Tuo and

Zhou, Tao
(2006)

*Relationship between on- and out-degree of WWW.*
Physica A: Statistical Mechanics and its Applications, 371 (2).
pp. 861-869.
Link to full text available through this repository.

- Abstract
In this paper, the relationship between the in-degree and out-degree of World-Wide Web is studies. At each time step, a new node with out-degree kout is added, where kout obeys the power-law distribution and its mean value is m. The analytical and simulation results suggest that the exponent of in-degree distribution would be ?i=2+1/m, depending on the average out-degree. This finding is supported by the empirical data, which has not been emphasized by the previous studies on directed networks.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Complex networks; Scale-free networks; Small-world networks; Disordered systems
- Centre
- CABDyN Complexity Centre

Liu, Jianguo,

Xuan, Zhao-Guo,

Dang, Yan-Zhong and

Wang, Zhong-Tuo
(2007)

*Weighted network properties of Chinese nature science basic research.*
Physica A: Statistical Mechanics and its Applications, 377 (1).
pp. 302-314.
Link to full text available through this repository.

- Abstract
Using the requisition papers of Chinese Nature Science Basic Research in management and information department, we construct the weighted network of research areas (WNRA). In WNRA, two research areas, which is represented by the subject codes, are considered to be connected if they have been filled in one or more requisition papers. The edge weight is defined as the number of requisition papers which have been filled in the same pairs of codes. The node strength is defined as the number of requisition papers which have been filled in this code, including the papers which have been filled in it alone. Here we study a variety of nonlocal statistics for WNRA, such as typical distance between research areas and measure of centrality such as betweenness. These statistical characteristics can illuminate the global development trend of Chinese scientific study. It is also helpful to adjust the code system to reflect the real status more accurately. Finally, we present a plausible model for the formation and structure of WNRA with the observed properties.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Complex networks; Chinese nature science basic research; Weighted networks
- Centre
- CABDyN Complexity Centre

López, Eduardo,

Carmi, Shai,

Havlin, Shlomo,

Buldyrev, Sergey and

Stanley, Eugene
(2006)

*Anomalous electrical and frictionless flow conductance in complex networks.*
Physica D: Nonlinear Phenomena, 224 (1-2).
pp. 69-76.
Link to full text available through this repository.

- Abstract
We study transport properties such as electrical and frictionless flow conductance on scale-free and Erdős–Rényi networks. We consider the conductance G between two arbitrarily chosen nodes where each link has the same unit resistance. Our theoretical analysis for scale-free networks predicts a broad range of values of G, with a power-law tail distribution , where gG=2λ−1, where λ is the decay exponent for the scale-free network degree distribution. We confirm our predictions by simulations of scale-free networks solving the Kirchhoff equations for the conductance between a pair of nodes. The power-law tail in leads to large values of G, thereby significantly improving the transport in scale-free networks, compared to Erdős–Rényi networks where the tail of the conductivity distribution decays exponentially. Based on a simple physical ‘transport backbone’ picture we suggest that the conductances of scale-free and Erdős–Rényi networks can be approximated by ckAkB/(kA+kB) for any pair of nodes A and B with degrees kA and kB. Thus, a single quantity c, which depends on the average degree of the network, characterizes transport on both scale-free and Erdős–Rényi networks. We determine that c tends to 1 for increasing , and it is larger for scale-free networks. We compare the electrical results with a model for frictionless transport, where conductance is defined as the number of link-independent paths between A and B, and find that a similar picture holds. The effects of distance on the value of conductance are considered for both models, and some differences emerge. Finally, we use a recent data set for the AS (autonomous system) level of the Internet and confirm that our results are valid in this real-world example.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Complex networks; Transport; Diffusion; Conductance; Scaling
- Centre
- CABDyN Complexity Centre

López, Eduardo,

Carmi, Shai,

Wu, Zheng,

Havlin, Shlomo and

Stanley, Eugene
(2007)

*Transport between multiple users in complex networks.*
European Physical Journal B: Condensed Matter and Complex Systems, 57 (2).
pp. 165-174.
Link to full text available through this repository.

- Abstract
We study the transport properties of model networks such as scale-free and Erdös-Rényi networks as well as a real network. We consider few possibilities for the transport problem. We start by studying the conductance G between two arbitrarily chosen nodes where each link has the same unit resistance. Our theoretical analysis for scale-free networks predicts a broad range of values of G, with a power-law tail distribution FSF(G) ~ G-gGSF(G)G−gG , where gG=2λ-1, and λ is the decay exponent for the scale-free network degree distribution. The power-law tail in ΦSF(G) leads to large values of G, thereby significantly improving the transport in scale-free networks, compared to Erdös-Rényi networks where the tail of the conductivity distribution decays exponentially. We develop a simple physical picture of the transport to account for the results. The other model for transport is the max-flow model, where conductance is defined as the number of link-independent paths between the two nodes, and find that a similar picture holds. The effects of distance on the value of conductance are considered for both models, and some differences emerge. We then extend our study to the case of multiple sources ans sinks, where the transport is defined between two groups of nodes. We find a fundamental difference between the two forms of flow when considering the quality of the transport with respect to the number of sources, and find an optimal number of sources, or users, for the max-flow case. A qualitative (and partially quantitative) explanation is also given.

- Item type
- Article
- Subject(s)
- UNSPECIFIED
- Uncontrolled keywords
- Networks and genealogical trees; Classical transport; Complex networks
- Centre
- CABDyN Complexity Centre

This list was generated on **Mon Feb 19 21:30:05 2018 WET**.