Community detection by label propagation with compression of flow

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm

The effects of overtaking strategy in the Nagel-Schreckenberg model

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, we proposed the NSOS model by adding the overtaking strategy (OS). In our model, overtaking vehicles are randomly selected with probability $q$ at each time step, and the successful overtaking is determined by their velocities. We observed that (i) traffic jams still occur in the NSOS model; (ii) OS increases the traffic flow in the regime where the densities exceed the maximum flow density. We also studied the phase transition (from free flow phase to jammed phase) of the NSOS model by analyzing the overtaking success rate, order parameter, relaxation time and correlation function, respectively. It was shown that the NSOS model differs from the NS model mainly in the jammed regime, and the influence of OS on the transition density is dominated by the braking probability $p$Comment: 9 pages, 20 figures, to be published in The European Physical Journal B (EPJB

Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.Comment: 16 pages, 11 figure

Multifractal and Network Analysis of Phase Transition

Many models and real complex systems possess critical thresholds at which the systems shift from one sate to another. The discovery of the early warnings of the systems in the vicinity of critical point are of great importance to estimate how far a system is from a critical threshold. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the fluctuation and geometrical structures of magnetization time series of two-dimensional Ising model around critical point. The Hurst exponent has been confirmed to be a good indicator of phase transition. Increase of the multifractality of the time series have been observed from generalized Hurst exponents and singularity spectrum. Both Long-term correlation and broad probability density function are identified to be the sources of multifractality of time series near critical regime. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties of magnetization time series from complex network perspective. Evolution of the topology quantities such as clustering coefficient, average degree, average shortest path length, density, assortativity and heterogeneity serve as early warnings of phase transition. Those methods and results can provide new insights about analysis of phase transition problems and can be used as early warnings for various complex systems.Comment: 23 pages, 11 figure

Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application

The limited penetrable horizontal visibility algorithm is a new time analysis tool and is a further development of the horizontal visibility algorithm. We present some exact results on the topological properties of the limited penetrable horizontal visibility graph associated with random series. We show that the random series maps on a limited penetrable horizontal visibility graph with exponential degree distribution $P(k)\sim exp[-\lambda (k-2\rho-2)], \lambda = ln[(2\rho+3)/(2\rho+2)],\rho=0,1,2,...,k=2\rho+2,2\rho+3,...$, independent of the probability distribution from which the series was generated. We deduce the exact expressions of the mean degree and the clustering coefficient and demonstrate the long distance visibility property. Numerical simulations confirm the accuracy of our theoretical results. We then examine several deterministic chaotic series (a logistic map, the H$\acute{e}$non map, the Lorentz system, and an energy price chaotic system) and a real crude oil price series to test our results. The empirical results show that the limited penetrable horizontal visibility algorithm is direct, has a low computational cost when discriminating chaos from uncorrelated randomness, and is able to measure the global evolution characteristics of the real time series.Comment: 23 pages, 12 figure

Structure of the Global Plastic Waste Trade Network and the Impact of China's Import Ban

Millions of tonnes (teragrams) of plastic waste are traded around the world every year, which plays an important role in partially substituting virgin plastics as a source of raw materials in plastic product manufacturing. In this paper, global plastic waste trade networks (GPWTNs) from 1988 to 2017 are established using the UN-Comtrade database. The spatiotemporal evolution of the GPWTNs is analyzed. Attention is given to the country ranks, inter- and intra-continental trade flows, and geo-visual communities in the GPWTNs. We also evaluate the direct and indirect impacts of China’s plastic waste import ban on the GPWTNs. The results show that the GPWTNs have small-world and scale-free properties and a core-periphery structure. The geography of the plastic waste trade is structured by Asia as the dominant importer and North America and Europe as the largest sources of plastic waste. China is the unrivaled colossus in the global plastic waste trade. After China’s import ban, the plastic waste trade flows have been largely redirected to Southeast Asian countries. Compared with import countries, export countries are more important for the robustness of GPWTNs. Clearly, developed countries will not announce bans on plastic waste exports; these countries have strong motivation to continue to shift plastic waste to poorer countries. However, the import bans from developing countries will compel developed countries to build new disposal facilities and deal with their plastic waste domestically

A multiple perspective method for urban subway network robustness analysis

Most network research studying the robustness of critical infrastructure networks focuses on a particular aspect and does not take the entire system into consideration. We develop a general methodological framework for studying network robustness from multiple perspectives, i.e., Robustness assessment based on percolation theory, vulnerability analysis, and controllability analysis. Meanwhile, We use this approach to examine the Shanghai subway network in China. Specifically, (1) the topological properties of the subway network are quantitatively analyzed using network theory; (2) The phase transition process of the subway network under both random and deliberate attacks are acquired (3) Critical dense areas that are most likely to be the target of terrorist attacks are identified, vulnerability values of these critical areas are obtained; (4) The minimum number of driver nodes for controlling the whole network is calculated. Results show that the subway network exhibits characteristics similar to a scale-free network with low robustness to deliberate attacks. Meanwhile, we identify the critical area within which disruptions produce large performance losses. Our proposed method can be applied to other infrastructure networks and can help decision makers develop optimal protection strategies