Opinion mining of text documents written in Macedonian language

The ability to extract public opinion from web portals such as review sites, social networks and blogs will enable companies and individuals to form a view, an attitude and make decisions without having to do lengthy and costly researches and surveys. In this paper machine learning techniques are used for determining the polarity of forum posts on kajgana which are written in Macedonian language. The posts are classified as being positive, negative or neutral. We test different feature metrics and classifiers and provide detailed evaluation of their participation in improving the overall performance on a manually generated dataset. By achieving 92% accuracy, we show that the performance of systems for automated opinion mining is comparable to a human evaluator, thus making it a viable option for text data analysis. Finally, we present a few statistics derived from the forum posts using the developed system.Comment: In press, MASA proceeding

Random walks on networks: cumulative distribution of cover time

We derive an exact closed-form analytical expression for the distribution of the cover time for a random walk over an arbitrary graph. In special case, we derive simplified exact expressions for the distributions of cover time for a complete graph, a cycle graph, and a path graph. An accurate approximation for the cover time distribution, with computational complexity of O(2n), is also presented. The approximation is numerically tested only for graphs with n<=1000 nodes

Analytically solvable processes on networks

We introduce a broad class of analytically solvable processes on networks. In the special case, they reduce to random walk and consensus process - two most basic processes on networks. Our class differs from previous models of interactions (such as stochastic Ising model, cellular automata, infinite particle system, and voter model) in several ways, two most important being: (i) the model is analytically solvable even when the dynamical equation for each node may be different and the network may have an arbitrary finite graph and influence structure; and (ii) in addition, when local dynamic is described by the same evolution equation, the model is decomposable: the equilibrium behavior of the system can be expressed as an explicit function of network topology and node dynamicsComment: 10 pages, 3 figure

A unifying definition of synchronization for dynamical systems

We propose a unified definition for synchronization. By example we show that the synchronization phenomena discussed in the dynamical systems literature can be described within the framework of this definition.Comment: 4 pages, no figures, submitted for publicatio

Tunneling of electrons via rotor-stator molecular interfaces: combined ab initio and model study

Tunneling of electrons through rotor-stator anthracene aldehyde molecular interfaces is studied with a combined ab initio and model approach. Molecular electronic structure calculated from first principles is utilized to model different shapes of tunneling barriers. Together with a rectangular barrier, we also consider a sinusoidal shape that captures the effects of the molecular internal structure more realistically. Quasiclassical approach with the Simmons' formula for current density is implemented. Special attention is paid on conformational dependence of the tunneling current. Our results confirm that the presence of the side aldehyde group enhances the interesting electronic properties of the pure anthracene molecule, making it a bistable system with geometry dependent transport properties. We also investigate the transition voltage and we show that confirmation dependent field emission could be observed in these molecular interfaces at realistically low voltages. The present study accompanies our previous work where we investigated the coherent transport via strongly coupled delocalized orbital by application of Non-equilibrium Green's Function Formalism

Modeling the Spread of Multiple Contagions on Multilayer Networks

A susceptible-infected-susceptible (SIS) model of multiple contagions on multilayer networks is developed to incorporate different spreading channels and disease mutations. The basic reproduction number for this model is estimated analytically. In a special case when considering only compartmental models, we analytically analyze an example of a model with a mutation driven strain persistence characterized by the absence of an epidemic threshold. This model is not related to the network topology and can be observed in both compartmental models and models on networks. The novel multiple-contagion SIS model on a multilayer network could help in the understanding of other spreading phenomena including communicable diseases, cultural characteristics, addictions, or information spread through e-mail messages, web blogs, and computer networks

Stacking and stability

Stacking is a general approach for combining multiple models toward greater predictive accuracy. It has found various application across different domains, ensuing from its meta-learning nature. Our understanding, nevertheless, on how and why stacking works remains intuitive and lacking in theoretical insight. In this paper, we use the stability of learning algorithms as an elemental analysis framework suitable for addressing the issue. To this end, we analyze the hypothesis stability of stacking, bag-stacking, and dag-stacking and establish a connection between bag-stacking and weighted bagging. We show that the hypothesis stability of stacking is a product of the hypothesis stability of each of the base models and the combiner. Moreover, in bag-stacking and dag-stacking, the hypothesis stability depends on the sampling strategy used to generate the training set replicates. Our findings suggest that 1) subsampling and bootstrap sampling improve the stability of stacking, and 2) stacking improves the stability of both subbagging and bagging.Comment: 15 pages, 1 figur

On the structure of the world economy: An absorbing Markov chain approach

The expansion of global production networks has raised many important questions about the interdependence among countries and how future changes in the world economy are likely to affect the countries' positioning in global value chains. We are approaching the structure and lengths of value chains from a completely different perspective than has been available so far. By assigning a random endogenous variable to a network linkage representing the number of intermediate sales/purchases before absorption (final use or value added), the discrete-time absorbing Markov chains proposed here shed new light on the world input/output networks. The variance of this variable can help assess the risk when shaping the chain length and optimize the level of production. Contrary to what might be expected simply on the basis of comparative advantage, the results reveal that both the input and output chains exhibit the same quasi-stationary product distribution. Put differently, the expected proportion of time spent in a state before absorption is invariant to changes of the network type. Finally, the several global metrics proposed here, including the probability distribution of global value added/final output, provide guidance for policy makers when estimating the resilience of world trading system and forecasting the macroeconomic developments

Stability of decision trees and logistic regression

Decision trees and logistic regression are one of the most popular and well-known machine learning algorithms, frequently used to solve a variety of real-world problems. Stability of learning algorithms is a powerful tool to analyze their performance and sensitivity and subsequently allow researchers to draw reliable conclusions. The stability of these two algorithms has remained obscure. To that end, in this paper, we derive two stability notions for decision trees and logistic regression: hypothesis and pointwise hypothesis stability. Additionally, we derive these notions for L2-regularized logistic regression and confirm existing findings that it is uniformly stable. We show that the stability of decision trees depends on the number of leaves in the tree, i.e., its depth, while for logistic regression, it depends on the smallest eigenvalue of the Hessian matrix of the cross-entropy loss. We show that logistic regression is not a stable learning algorithm. We construct the upper bounds on the generalization error of all three algorithms. Moreover, we present a novel stability measuring framework that allows one to measure the aforementioned notions of stability. The measures are equivalent to estimates of expected loss differences at an input example and then leverage bootstrap sampling to yield statistically reliable estimates. Finally, we apply this framework to the three algorithms analyzed in this paper to confirm our theoretical findings and, in addition, we discuss the possibilities of developing new training techniques to optimize the stability of logistic regression, and hence decrease its generalization error.Comment: 13 page

Beyond network structure: How heterogenous susceptibility modulates the spread of epidemics

The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that--for the SIS model--differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.Comment: 13 pages, 2 figure