Jinshan Wu

NETWORK OF ECONOPHYSICISTS: A WEIGHTED NETWORK TO INVESTIGATE THE DEVELOPMENT OF ECONOPHYSICS

The development of Econophysics is studied from the perspective of scientific communication networks. Papers in Econophysics published from 1992 to 2003 are collected. Then a weighted and directed network of scientific communication, including collaboration, citation and personal discussion, is constructed. Its static geometrical properties, including degree distribution, weight distribution, weight per degree, and betweenness centrality, give a nice overall description of the research works. The way we introduced here to measure the weight of connections can be used as a general one to construct weighted network.

Accuracy and precision of methods for community identification in weighted networks

Different algorithms, which take both links and link weights into account for the community structure of weighted networks, have been reported recently. Based on the measure of similarity among community structures introduced in our previous work, in this paper, accuracy and precision of three algorithms are investigated. Results show that Potts model based algorithm and weighted extremal optimization (WEO) algorithm work well on both dense or sparse weighted networks, while weighted Girvan–Newman (WGN) algorithm works well only for relatively sparse networks.

The spread of infectious disease on complex networks with household-structure

In this paper we study the household-structure SIS epidemic spreading on general complex networks. The household structure gives us the way to distinguish inner and the outer infection rate. Unlike household-structure models on homogenous networks, such as regular and random networks, here we consider heterogeneous networks with arbitrary degree distribution p(k). First we introduce the epidemic model. Then rate equations under mean field appropriation and computer simulations are used here to analyze our model. Some unique phenomena only existing in divergent network with household structure is found, while we also get some similar conclusions that some simple geometrical quantities of networks have important impression on infection property of infectous disease. It seems that in our model even when local cure rate is greater than inner infection rate in every household, disease still can spread on scale-free network. It implies that no disease is spreading in every single household, but for the whole network, disease is spreading. Since our society network seems like this structure, maybe this conclusion remind us that during disease spreading we should pay more attention on network structure than local cure condition.

The effect of weight on community structure of networks

The effect of weight on community structures is investigated in this paper. We use weighted modularity Qw to evaluate the partitions and weighted extremal optimization algorithm to detect communities. Starting from empirical and idealized weighted networks, the matching between weights and edges are disturbed. Then using similarity function S to measure the difference between community structures, it is found that the redistribution of weights does strongly affect the community structure especially in dense networks. This indicates that the community structure in networks is a suitable property to reflect the role of weight.

Emergence of global preferential attachment from local interaction

Global degree/strength-based preferential attachment is widely used as an evolution mechanism of networks. But it is hard to believe that any individual can get global information and shape the network architecture based on it. In this paper, it is found that the global preferential attachment emerges from the local interaction models, including the distance-dependent preferential attachment (DDPA) evolving model of weighted networks (Li et al 2006 New J. Phys. 8 72), the acquaintance network model (Davidsen et al 2002 Phys. Rev. Lett. 88 128701) and the connecting nearest-neighbor (CNN) model (Vazquez 2003 Phys. Rev. E 67 056104). For the DDPA model and the CNN model, the attachment rate depends linearly on the degree or vertex strength, whereas for the acquaintance network model, the dependence follows a sublinear power law. It implies that for the evolution of social networks, local contact could be more fundamental than the presumed global preferential attachment.

Do scientists trace hot topics

Do scientists follow hot topics in their scientific investigations? In this paper, by performing analysis to papers published in the American Physical Society (APS) Physical Review journals, it is found that papers are more likely to be attracted by hot fields, where the hotness of a field is measured by the number of papers belonging to the field. This indicates that scientists generally do follow hot topics. However, there are qualitative differences among scientists from various countries, among research works regarding different number of authors, different number of affiliations and different number of references. These observations could be valuable for policy makers when deciding research funding and also for individual researchers when searching for scientific projects.

From sparse to dense and from assortative to disassortative in online social networks

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Modelling weighted networks using connection count

Weight-driven and degree-driven evolutionary models for weighted networks are proposed, with a distance-dependent mechanism to increase the clustering coefficient. Compared with the well-known BA model there are two generalizations. Firstly, introducing connection count and converting it into edge weight, both new and old vertices can attempt to build up connections. When a new edge is inserted between already connected nodes, the connection count (and thereby the weight) of the corresponding link is increased. Secondly, the distribution of local path distance is also used as a reference in the preferential attachment. The models show some interesting results including scale-free distributions on degree, vertex weight and edge weight. Even the pure distance-dependent preferential attachment model shows all these typical behaviours and a high cluster coefficient. The concepts of reconnecting and converting count as weight are not new and have been used in empirical studies of weighted networks but are first used to model weighted networks.

The correlation between editorial delay and the ratio of highly cited papers in Nature, Science and Physical Review Letters

Ideally, in a reviewing process, it is generally easier for referees to make faster and more reliable decisions for high quality papers, which ideally and on average will later attract more citations. Therefore, it is possible that the editorial delay time—the time between dates of submission and acceptance or publication—is correlated to the number of received citations, as has been weakly confirmed by previous studies. In this study, we propose a different measure for this correlation. Instead of directly calculating the correlation coefficient between the editorial delay and the number of citations, we define a ratio of above median highly cited papers and perform correlation analysis between editorial delay and that ratio for all academic papers published in Nature, Science and Physical Review Letters. Using this alternative measure, we find that on average papers with shorter editorial delay do have larger probabilities of becoming highly cited papers.

Heat transport in quantum spin chains: Relevance of integrability

We investigate heat transport in various quantum spin chains, using the projector operator technique. We find that anomalous heat transport is linked not to the integrability of the Hamiltonian, but to whether it can be mapped to a model of non-interacting fermions. Our results also suggest how seemingly anomalous transport may occur at low temperatures in a much wider class of models.