Phase transitions in social networks

We study a model of network with clustering and desired node degree. The original purpose of the model was to describe optimal structures of scientific collaboration in the European Union. The model belongs to the family of exponential random graphs. We show by numerical simulations and analytical considerations how a very simple Hamiltonian can lead to surprisingly complicated and eventful phase diagram.Comment: 8 pages, 8 figure

A continuous time random walk model for financial distributions

We apply the formalism of the continuous time random walk to the study of financial data. The entire distribution of prices can be obtained once two auxiliary densities are known. These are the probability densities for the pausing time between successive jumps and the corresponding probability density for the magnitude of a jump. We have applied the formalism to data on the US dollar/Deutsche Mark future exchange, finding good agreement between theory and the observed data.Comment: 14 pages, 5 figures, revtex4, submitted for publicatio

Theoretical approach and impact of correlations on the critical packet generation rate in traffic dynamics on complex networks

Using the formalism of the biased random walk in random uncorrelated networks with arbitrary degree distributions, we develop theoretical approach to the critical packet generation rate in traffic based on routing strategy with local information. We explain microscopic origins of the transition from the flow to the jammed phase and discuss how the node neighbourhood topology affects the transport capacity in uncorrelated and correlated networks.Comment: 6 pages, 5 figure

Evolving Clustered Random Networks

We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus serve as generic models for studying the impacts of degree distributions and clustering on dynamical processes as well as null models for detecting other structural properties in empirical networks

Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit

In recent years, there has been a surge of interest in community detection algorithms for complex networks. A variety of computational heuristics, some with a long history, have been proposed for the identification of communities or, alternatively, of good graph partitions. In most cases, the algorithms maximize a particular objective function, thereby finding the `right' split into communities. Although a thorough comparison of algorithms is still lacking, there has been an effort to design benchmarks, i.e., random graph models with known community structure against which algorithms can be evaluated. However, popular community detection methods and benchmarks normally assume an implicit notion of community based on clique-like subgraphs, a form of community structure that is not always characteristic of real networks. Specifically, networks that emerge from geometric constraints can have natural non clique-like substructures with large effective diameters, which can be interpreted as long-range communities. In this work, we show that long-range communities escape detection by popular methods, which are blinded by a restricted `field-of-view' limit, an intrinsic upper scale on the communities they can detect. The field-of-view limit means that long-range communities tend to be overpartitioned. We show how by adopting a dynamical perspective towards community detection (Delvenne et al. (2010) PNAS:107: 12755-12760; Lambiotte et al. (2008) arXiv:0812.1770), in which the evolution of a Markov process on the graph is used as a zooming lens over the structure of the network at all scales, one can detect both clique- or non clique-like communities without imposing an upper scale to the detection. Consequently, the performance of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of equally good results in real networks with local, sparser connectivity.Comment: 20 pages, 6 figure

Probing and controlling fluorescence blinking of single semiconductor nanoparticles

In this review we present an overview of the experimental and theoretical development on fluorescence intermittency (blinking) and the roles of electron transfer in semiconductor crystalline nanoparticles. Blinking is a very interesting phenomenon commonly observed in single molecule/particle experiments. Under continuous laser illumination, the fluorescence time trace of these single nanoparticles exhibit random light and dark periods. Since its first observation in the mid-1990s, this intriguing phenomenon has attracted wide attention among researchers from many disciplines. We will first present the historical background of the discovery and the observation of unusual inverse power-law dependence for the waiting time distributions of light and dark periods. Then, we will describe our theoretical modeling efforts to elucidate the causes for the power-law behavior, to probe the roles of electron transfer in blinking, and eventually to control blinking and to achieve complete suppression of the blinking, which is an annoying feature in many applications of quantum dots as light sources and fluorescence labels for biomedical imaging

Spreading to localized targets in complex networks.

As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and news propagation, the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.We thank an anonymous reviewer for helpful suggestions which improve this paper. This work is supported by the National Natural Science Foundation of China (Nos 61603046 and 11547188), Natural Science Foundation of Beijing (No. 16L00077) and the Young Scholar Program of Beijing Normal University (No. 2014NT38)