arXiv subjects

Jian-Guo Liu

Publications and source records attributed to Jian-Guo Liu.

177 records · Page 10Linked to original sources

arXiv2009

Information filtering based on transferring similarity

In this Brief Report, we propose a new index of user similarity, namely the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E {\bf 73} 026120 (2006)], and is relevant to the missing link prediction problem.

physics.data-an↗

arXiv2008

Empirical analysis on a keyword-based semantic system

Keywords in scientific articles have found their significance in information filtering and classification. In this article, we empirically investigated statistical characteristics and evolutionary properties of keywords in a very famous journal, namely Proceedings of the National Academy of Science of the United States of America (PNAS), including frequency distribution, temporal scaling behavior, and decay factor. The empirical results indicate that the keyword frequency in PNAS approximately follows a Zipf's law with exponent 0.86. In addition, there is a power-low correlation between the cumulative number of distinct keywords and the cumulative number of keyword occurrences. Extensive empirical analysis on some other journals' data is also presented, with decaying trends of most popular keywords being monitored. Interestingly, top journals from various subjects share very similar decaying tendency, while the journals of low impact factors exhibit completely different behavior. Those empirical characters may shed some light on the in-depth understanding of semantic evolutionary behaviors. In addition, the analysis of keyword-based system is helpful for the design of corresponding recommender systems.

physics.data-an↗

arXiv2007

Generalized monotone schemes, discrete paths of extrema, and discrete entropy conditions

Solutions to conservation laws satisfy the monotonicity property: the number of local extrema is a non-increasing function of time, and local maximum/minimum values decrease/increase monotonically in time. This paper investigates this property from a numerical standpoint. We introduce a class of fully discrete in space and time, high order accurate, difference schemes, called generalized monotone schemes. Convergence toward the entropy solution is proven via a new technique of proof, assuming that the initial data has a finite number of extremum values only, and the flux-function is strictly convex. We define discrete paths of extrema by tracking local extremum values in the approximate solution. In the course of the analysis we establish the pointwise convergence of the trace of the solution along a path of extremum. As a corollary, we obtain a proof of convergence for a MUSCL-type scheme being second order accurate away from sonic points and extrema.

math.NA↗

arXiv2006

Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity

In this article, we proposed a susceptible-infected model with identical infectivity, in which, at every time step, each node can only contact a constant number of neighbors. We implemented this model on scale-free networks, and found that the infected population grows in an exponential form with the time scale proportional to the spreading rate. Further more, by numerical simulation, we demonstrated that the targeted immunization of the present model is much less efficient than that of the standard susceptible-infected model. Finally, we investigated a fast spreading strategy when only local information is available. Different from the extensively studied path finding strategy, the strategy preferring small-degree nodes is more efficient than that preferring large-degree nodes. Our results indicate the existence of an essential relationship between network traffic and network epidemic on scale-free networks.

physics.bio-ph↗

arXiv2006

Relationship Between Structural Characters and Synchronizability of Scale-free Networks

Using Memory Tabu Search(MTS) algorithm, we investigate the relationship between structural characters and synchronizability of scale-free networks by maximizing and minimizing the ratio $Q$ of the eigenvalues of the coupling matrix by edge-intercrossing procedures. The numerical results indicate that clustering coefficient $C$, maximal betweenness $B_{max}$ are two most important factors to scale-free network synchronizability, and assortative coefficient $r$ and average distance $D$ are the secondary ones. Moreover, the average degree $ $ affects the relationship between above structural characters and synchronizability of scale-free networks, and the minimal $Q$ decreases when $ $ increases.

physics.data-an↗

arXiv2006

Weighted Network of Chinese Nature Science Basic Research

Using the requisition papers of Chinese Nature Science Basic Research in management and information department, we construct the weighted network of research areas({\bf WRAN}) represented by the subject codes. In WRAN, two research areas are considered connected if they have been filled in at least one requisition paper. The edge weight is defined as the number of requisition papers which have filled in the same pairs of codes. The node strength is defined as the number of requisition papers which have filled in this code, including the papers which have filled in it only. Here we study a variety of nonlocal statistics for these networks, such as typical distances between research areas through the network, and measures of centrality such as betweenness. These statistics 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 networks with the observed properties.

physics.soc-ph↗

arXiv2006

Growing Scale-free Small-world Networks with Tunable Assortative Coefficient

In this paper, we propose a simple rule that generates scale-free small-world networks with tunable assortative coefficient. These networks are constructed by two-stage adding process for each new node. The model can reproduce scale-free degree distributions and small-world effect. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$ by adjusting the parameter: the preferential exponent $β$. The model can unify the characterization of both assortative and disassortative networks.

physics.soc-ph↗

arXiv2005

Self-learning Mutual Selection Model for Weighted Networks

In this paper, we propose a self-learning mutual selection model to characterize weighted evolving networks. By introducing the self-learning probability $p$ and the general mutual selection mechanism, which is controlled by the parameter $m$, the model can reproduce scale-free distributions of degree, weight and strength, as found in many real systems. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$, depending on the parameters $m$ and $p$. The model can unify the characterization of both assortative and disassortative weighted networks. Also, we find that self-learning may contribute to the assortative mixing of social networks.

physics.soc-ph↗

arXiv2005

Comment on ``Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality"

In this comment, we investigate a common used algorithm proposed by Newman [M. E. J. Newman, Phys. Rev. E {\bf 64}, 016132(2001)] to calculate the betweenness centrality for all vertices. The inaccurateness of Newman's algorithm is pointed out and a corrected algorithm, also with O($MN$) time complexity, is given. In addition, the comparison of calculating results for these two algorithm aiming the protein interaction network of Yeast is shown.

physics.soc-ph↗

arXiv2005

Multistage Random Growing Small-World Networks with Power-law degree Distribution

In this paper, a simply rule that generates scale-free networks with very large clustering coefficient and very small average distance is presented. These networks are called {\bf Multistage Random Growing Networks}(MRGN) as the adding process of a new node to the network is composed of two stages. The analytic results of power-law exponent $γ=3$ and clustering coefficient $C=0.81$ are obtained, which agree with the simulation results approximately. In addition, the average distance of the networks increases logarithmical with the number of the network vertices is proved analytically. Since many real-life networks are both scale-free and small-world networks, MRGN may perform well in mimicking reality.

physics.comp-ph↗

arXiv2005

A directed network model for World-Wide Web

In this paper, a directed network model for world-wide web is presented. The out-degree of the added nodes are supposed to be scale-free and its mean value is $m$. This model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, the in-degree distribution obeys the power-law form with the exponent $γ=2+1/m$, 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.

physics.soc-ph↗

arXiv2005

Optimization of scale-free network for random failures

It has been found that the networks with scale-free distribution are very resilient to random failures. The purpose of this work is to determine the network design guideline which maximize the network robustness to random failures with the average number of links per node of the network is constant. The optimal value of the distribution exponent and the minimum connectivity to different network size are given in this paper. Finally, the optimization strategy how to improve the evolving network robustness is given.

cond-mat.stat-mech↗

arXiv2005

Optimization of robustness of scale-free network to random and targeted attacks

The scale-fee networks, having connectivity distribution $P(k)\sim k^{-α}$ (where $k$ is the site connectivity), is very resilient to random failures but fragile to intentional attack. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attack maximum while keeping the average connectivity $ $ per node constant. We find that when $ =3$ the robustness of the scale-free networks reach its maximum value if the minimal connectivity $m=1$, but when $ $ is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity $m$ gets larger.

cond-mat.stat-mech↗

arXiv2005

On Incompressible Navier-Stokes Dynamics: A New Approach for Analysis and Computation

We show that in bounded domains with no-slip boundary conditions, the Navier-Stokes pressure can be determined in a such way that it is strictly dominated by viscosity. As a consequence, in a general domain we can treat the Navier-Stokes equations as a perturbed vector diffusion equation, instead of as a perturbed Stokes system. To illustrate the advantages of this view, we provide a simple proof of the unconditional stability of a difference scheme that is implicit only in viscosity and explicit in both pressure and convection terms, requiring no solution of stationary Stokes systems or inf-sup conditions.

math.AP↗

arXiv2005

Divorcing pressure from viscosity in incompressible Navier-Stokes dynamics

We show that in bounded domains with no-slip boundary conditions, the Navier-Stokes pressure can be determined in a such way that it is strictly dominated by viscosity. As a consequence, in a general domain we can treat the Navier-Stokes equations as a perturbed vector diffusion equation, instead of as a perturbed Stokes system. We illustrate the advantages of this view in a number of ways. In particular, we provide simple proofs of (i) local-in-time existence and uniqueness of strong solutions for an unconstrained formulation of the Navier-Stokes equations, and (ii) the unconditional stability and convergence of difference schemes that are implicit only in viscosity and explicit in both pressure and convection terms, requiring no solution of stationary Stokes systems or inf-sup conditions.

math.AP↗