Transition to hexagonal pattern under the variation of intrinsic length scales of a reaction diffusion system

The intrinsic length scales of a reaction diffusion system (Gierer-Meinhardt model)is varied by quasi-statically changing the diffusion constant of the activator and a transition from rolls to hexagon is detected. The transition is hysteretic or first order like. From stability analysis, we also analytically show the possibility of such transitions.Comment: One .tex file and four .ps file

Scaling of internode distances in weighted complex networks

We extend the previously observed scaling equation connecting the internode distances and nodes' degrees onto the case of weighted networks. We show that the scaling takes a similar form in the empirical data obtained from networks characterized by different relations between node's strength and its degree. In the case of explicit equation for s(k) (e.g. linear or scale-free), the new coefficients of scaling equation can be easily obtained. We support our analysis with numerical simulations for Erdos-Renyi random graphs with different weight distributions.Comment: 9 pages, 4 figures, submitted to International Journal of Modern Physics

Impact of lexical and sentiment factors on the popularity of scientific papers

We investigate how textual properties of scientific papers relate to the number of citations they receive. Our main finding is that correlations are non-linear and affect differently most-cited and typical papers. For instance, we find that in most journals short titles correlate positively with citations only for the most cited papers, for typical papers the correlation is in most cases negative. Our analysis of 6 different factors, calculated both at the title and abstract level of 4.3 million papers in over 1500 journals, reveals the number of authors, and the length and complexity of the abstract, as having the strongest (positive) influence on the number of citations.Comment: 9 pages, 3 figures, 3 table

q-neighbor Ising model on a polarized network

In this paper, we have examined the interplay between the lobby size $q$ in the $q$-neighbor Ising model of opinion formation [Phys. Rev. E 92, 052105] and the level of overlap $v$ of two fully connected graphs. Results suggest that for each lobby size $q \ge 3$ there exists a specific level of overlap $v^*$ which destroys initially polarized clusters of opinions. By performing Monte-Carlo simulations, backed by an analytical approach we show that the dependence of the $v^*$ on the lobby size $q$ is far from trivial in the absence of temperature $T \rightarrow 0$, showing a clear maximum that additionally depends on the parity of $q$. On the other hand, the temperature is a destructive factor, its increase leads to the earlier collapse of polarized clusters but additionally brings a substantial decrease in the level of polarization

Determining crucial factors for the popularity of scientific articles

Using a set of over 70.000 records from PLOS One journal consisting of 37 lexical, sentiment and bibliographic variables we perform analysis backed with machine learning methods to predict the class of popularity of scientific papers defined by the number of times they have been viewed. Our study shows correlations among the features and recovers a threshold for the number of views that results in the best prediction results in terms of Matthew's correlation coefficient. Moreover, by creating a variable importance plot for random forest classifier, we are able to reduce the number of features while keeping similar predictability and determine crucial factors responsible for the popularity.Comment: 13 pages, 6 figure

The role of emotional variables in the classification and prediction of collective social dynamics

We demonstrate the power of data mining techniques for the analysis of collective social dynamics within British Tweets during the Olympic Games 2012. The classification accuracy of online activities related to the successes of British athletes significantly improved when emotional components of tweets were taken into account, but employing emotional variables for activity prediction decreased the classifiers' quality. The approach could be easily adopted for any prediction or classification study with a set of problem-specific variables.Comment: 16 pages, 9 figures, 2 tables and 1 appendi