Robustness of the avalanche dynamics in data packet transport on scale-free networks

We study the avalanche dynamics in the data packet transport on scale-free networks through a simple model. In the model, each vertex is assigned a capacity proportional to the load with a proportionality constant $1+a$. When the system is perturbed by a single vertex removal, the load of each vertex is redistributed, followed by subsequent failures of overloaded vertices. The avalanche size depends on the parameter $a$ as well as which vertex triggers it. We find that there exists a critical value $a_c$ at which the avalanche size distribution follows a power law. The critical exponent associated with it appears to be robust as long as the degree exponent is between 2 and 3, and is close in value to that of the distribution of the diameter changes by single vertex removal.Comment: 5 pages, 7 figures, final version published in PR

Betweenness centrality correlation in social networks

Scale-free (SF) networks exhibiting a power-law degree distribution can be grouped into the assortative, dissortative and neutral networks according to the behavior of the degree-degree correlation coefficient. Here we investigate the betweenness centrality (BC) correlation for each type of SF networks. While the BC-BC correlation coefficients behave similarly to the degree-degree correlation coefficients for the dissortative and neutral networks, the BC correlation is nontrivial for the assortative ones found mainly in social networks. The mean BC of neighbors of a vertex with BC $g_i$ is almost independent of $g_i$, implying that each person is surrounded by almost the same influential environments of people no matter how influential the person is.Comment: 4 pages, 4 figures, 1 tabl

Establishing links between organizational climate, employee well-being and historical patient outcomes

This research undertaken in collaboration with Queensland Health analysed the links between dimensions of workplace climate/employee well-being contained in a number of Queensland Health databases, including the Patient Satisfaction Survey, the Clinical Incident database, the compliments and complaints database, the Variable Life Adjusted Display (VLAD) Database and the Better Workplaces Staff Opinion Survey database. Queensland Health sought to identify in what ways workplace climate is related to patient outcomes using existing datasets collected within the Queensland Health Centre for Healthcare Improvement. The process of establishing links involved matching aggregated data for specific facilities (where possible), or failing that, larger facilities (e.g. Hospital), or the Health Service District. Once the datasets had been matched on location or facility, correlations were calculated between the aggregated scores. The results demonstrated links between the data sets. These links showed that a better workplace climate is associated with greater reported numbers of clinical incidents, especially “no harm” clinical incidents. There was also a link between workplace climate and patient compliments/complaints which show that unsolicited compliments received from patients and their families are clearly related to a number of positive aspects of workplace climate (workplace morale, role clarity, and appraisal and recognition) and individual morale. The results linking workplace climate and patient satisfaction showed that there is a strong positive relationship between overall patient satisfaction and role clarity, and a negative relationship between overall patient satisfaction and both workplace distress and excessive work demands. While these results relate to historical data and therefore should not be construed to reflect the current state of operation within Queensland Health, they are still indicative of some very important relationships. This is the first study to demonstrate that more positive clinical management practices, better perceptions of the workplace climate and better employee well-being are a reflection of a better incident reporting and learning culture in a health care organization, ultimately resulting in improved patient outcomes

Branching process approach for Boolean bipartite networks of metabolic reactions

The branching process (BP) approach has been successful in explaining the avalanche dynamics in complex networks. However, its applications are mainly focused on unipartite networks, in which all nodes are of the same type. Here, motivated by a need to understand avalanche dynamics in metabolic networks, we extend the BP approach to a particular bipartite network composed of Boolean AND and OR logic gates. We reduce the bipartite network into a unipartite network by integrating out OR gates, and obtain the effective branching ratio for the remaining AND gates. Then the standard BP approach is applied to the reduced network, and the avalanche size distribution is obtained. We test the BP results with simulations on the model networks and two microbial metabolic networks, demonstrating the usefulness of the BP approach

Classification of scale-free networks

While the emergence of a power law degree distribution in complex networks is intriguing, the degree exponent is not universal. Here we show that the betweenness centrality displays a power-law distribution with an exponent \eta which is robust and use it to classify the scale-free networks. We have observed two universality classes with \eta \approx 2.2(1) and 2.0, respectively. Real world networks for the former are the protein interaction networks, the metabolic networks for eukaryotes and bacteria, and the co-authorship network, and those for the latter one are the Internet, the world-wide web, and the metabolic networks for archaea. Distinct features of the mass-distance relation, generic topology of geodesics and resilience under attack of the two classes are identified. Various model networks also belong to either of the two classes while their degree exponents are tunable.Comment: 6 Pages, 6 Figures, 1 tabl

Sandpiles on multiplex networks

We introduce the sandpile model on multiplex networks with more than one type of edge and investigate its scaling and dynamical behaviors. We find that the introduction of multiplexity does not alter the scaling behavior of avalanche dynamics; the system is critical with an asymptotic power-law avalanche size distribution with an exponent $\tau = 3/2$ on duplex random networks. The detailed cascade dynamics, however, is affected by the multiplex coupling. For example, higher-degree nodes such as hubs in scale-free networks fail more often in the multiplex dynamics than in the simplex network counterpart in which different types of edges are simply aggregated. Our results suggest that multiplex modeling would be necessary in order to gain a better understanding of cascading failure phenomena of real-world multiplex complex systems, such as the global economic crisis.Comment: 7 pages, 7 figure