Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

/pdf/models-and-methods-in-social-network-analysis-46f9pqfov7.pdf

Models and methods in social network analysis

https://isiarticles.com/bundles/Article/pre/pdf/27039.pdf

Integrated process planning and scheduling by an agent-based ant colony optimization

A Bayesian network is a graphical probabilistic model that represents the conditional dependencies among uncertain variables, which can be both objective and subjective. We present a Bayesian network model for forecasting Association Football matches in which the subjective variables represent the factors that are important for prediction but which historical data fails to capture. The model (pi-football) was used to generate forecasts about the outcomes of the English Premier League (EPL) matches during season 2010/11 (but is easily extended to any football league). Forecasts were published online prior to the start of each match. We show that:(a)using an appropriate measure of forecast accuracy, the subjective information improved the model such that posterior forecasts were on par with bookmakers' performance; (b)using a standard profitability measure with discrepancy levels at >=5%, the model generates profit under maximum, mean, and common bookmakers' odds, even allowing for the bookmakers' built-in profit margin. Hence, compared with other published football forecast models, pi-football not only appears to be exceptionally accurate, but it can also be used to 'beat the bookies'.

/pdf/pi-football-a-bayesian-network-model-for-forecasting-1yeg6whsxm.pdf

pi-football: A Bayesian network model for forecasting Association Football match outcomes

In this paper, referring to a vapor compression refrigeration plant subjected to a commercially available cold store, a control algorithm, based on the fuzzy logic and able to select the most suitable compressor speed in function of the cold store air temperature, is presented. The main aim is to evaluate the energy saving obtainable when the fuzzy algorithm, which continuously regulates the compressor speed by an inverter, is employed to control the compressor refrigeration capacity instead of the classical thermostatic control, which imposes on/off cycles on the compressor that works at the nominal frequency of 50 Hz. The variation of the reciprocating compressor speed is obtained by controlling the compressor electric motor supply current frequency in the range 30–50 Hz, as it is not possible to consider values smaller than 30 Hz because of the lubrication troubles due to the splash system. In this range, two among the most suitable working fluids proposed for the R22 substitution, such as the R407C (R32/R125/R134a 23/25/52% in mass) and the R507 (R125/R143A 50/50% in mass) are tested. Comparing the compressor speed fuzzy control with the classical thermostatic control, frequently used in the cold stores and in other refrigeration systems, the experimental results show a meaningful energy saving equal even to about 13% when the R407C is used as a working fluid. In particular, to explain from the energy saving point of view the best performances of the refrigeration plant when the compressor speed varies, an exergetic analysis is realized. Besides, with regard to the inverter cost, the pay-back period determined is more than acceptable for the plant size examined.

Fuzzy control of the compressor speed in a refrigeration plant

We propose a framework for sports prediction using Bayesian inference and rule-based reasoning, together with an in-game time-series approach. The framework is novel in three ways. The framework consists of two major components: a rule-based reasoner and a Bayesian network component. The two different approaches cooperate in predicting the results of sports matches. It is motivated by the observation that sports matches are highly stochastic, but at the same time, the strategies of a team can be approximated by crisp logic rules. Furthermore, because of the rule-based component, our framework can give reasonably good predictions even when statistical data is scanty: it can be used to predict results of matches between teams which have had few previous encounters. Machine learning techniques have great difficulty in handling such situations of insufficient data. Second, our framework is able to consider many factors, such as current scores, morale, fatigue, skills, etc. when it predicts the results of sports matches: most previous work considered only one factor, usually the score. Third, in contrast to most previous work on sports results prediction, we use a knowledge-based in-game time-series approach to predict sports matches. This approach enables our framework to reflect the tides/flows of a sports match, making our predictions certainly more realistic, and somewhat more accurate. We have implemented a football results predictor called FRES (Football Result Expert System) based on this framework, and show that it gives reasonable and stable predictions.

A compound framework for sports results prediction: A football case study

This paper reviews the theory and applications of ant algorithms, new methods of discrete optimization based on the simulation of self-organized colony of biologic ants. The colony can be regarded as a multi-agent system where each agent is functioning independently by simple rules. Unlike the nearly primitive behavior of the agents, the behavior of the whole system happens to be amazingly reasonable. The ant algorithms have been extensively studied by European researchers from the mid-1990s. These algorithms have successfully been applied to solving many complex combinatorial optimization problems, such as the traveling salesman problem, the vehicle routing problem, the problem of graph coloring, the quadratic assignment problem, the problem of network-traffic optimization, the job-shop scheduling problem, etc. The ant algorithms are especially efficient for online optimization of processes in distributed nonstationary systems (for example, telecommunication network routing).

http://shtovba.vk.vntu.edu.ua/file/c6226ead2b1323fec51024a6c80c5a3b.pdf

Ant Algorithms: Theory and Applications

Soft computing methods for result prediction of football games based on fuzzy rules, neural networks and genetic programming techniques, are proposed in the article. The models are taking into account the following features of football teams: difference of infirmity factors; difference of dynamics profile; difference of ranks; host factor; personal score of the teams. Testing shows that the proposed models achieve a satisfactory estimation of the actual game results. The current work concludes with the recommendation of soft-computing techniques as a powerful approach, either for the creation of result prediction models of diverse sport championships, or as effective data extrapolation mechanisms in case of limited available statistics.

/pdf/soft-computing-based-result-prediction-of-football-games-nyi8xskuyo.pdf

Soft Computing-Based Result Prediction of Football Games

Whenever a problem to predict and ensure reliability of human-machine systems is posed, regression models are usually applied to evaluate the influence of different factors on faultlessness, exactitude, operating speed, and other characteristics of the operator performance. The Sugeno fuzzy knowledge bases are proposed to model multi-factor relations of reliability. It is shown that this approach allows to combine expert knowledge and analytical relations of the parametric reliability theory in operator activity models. The expert component of a model provides with a comprehensive interpretation, while analytical input-output relations make a model compact. Appropriate examples are presented to demonstrate advantages of the Sugeno knowledge base application to describe multi-factor reliability relations of human operator.

http://www.serhiy-shtovba.narod.ru/doc/Rotshtein_Shtovba__ARC_2009_1.pdf

Modeling of the human operator reliability with the aid of the Sugeno fuzzy knowledge base

In a fuzzy classifier, the maping “inputs–output” is described by the linguistic 〈If–then〉 rules, the antecedents in which contain the fuzzy terms “low,” “average,” “high,” and so on. To increase the correctness, the fuzzy classifier is learned by using experimental data. The problems with equal and different costs of various classification errors are discussed. A new criterion is offered for problems with undistinguishable types of errors, in addition to the two known criteria. A new one implies that the distance between the desired and real fuzzy results of classification for the cases of a wrong decision is weighted by the penalty factor. The learning criteria are generalized for problems of classification with the cost matrix. The conducted computer experiments on the wine recognition and heart disease diagnostics problems show that the best quality parameters of tuning fuzzy classifiers are achieved by a new learning criterion.

Analyzing the criteria for fuzzy classifier learning

Social networks sometimes become a medium for threats, insults and other components of cyberbullying. A huge number of people are involved in online social networks. Hence, a protection of network users from anti-social behavior is an important activity. One of the major tasks of such activity is automated detecting the toxic comments with threats, insults, obscene etc. The bag of words statistics and bag of symbols statistics are typical features for the toxic comments detection. The effect of syntactic dependencies in sentences on the quality of detection of the social network toxic comments is studied in the article for the first time. Syntactic dependences are relationships with proper nouns, personal pronouns, possessive pronouns, etc. Twenty syntactic features of sentences have been verified in the total. The paper shows that 3 additional specific features significantly improve the quality of toxic comments detection. These three features are: the number of dependences with proper nouns in the singular, the number of dependences that contain bad words, and the number of dependences between personal pronouns and bad words. The experiments are based on data from kaggle competition "Toxic Comment Classification Challenge". For our experiments, the original dataset with 159751 comments was reduced to 106590 comments due to problems with human-free extraction of the syntactic features. We use mean of the error rates for each types of misclassification as the metric of quality due to unbalanced dataset. A decision tree is used as a classifier. The decision trees were synthesized for two splitting rules: Gini index and deviance criterion.

/pdf/detection-of-social-network-toxic-comments-with-usage-of-1ow4ayfruk.pdf

Serhiy Shtovba

Papers

Ant Algorithms: Theory and Applications

Soft Computing-Based Result Prediction of Football Games

Modeling of the human operator reliability with the aid of the Sugeno fuzzy knowledge base

Analyzing the criteria for fuzzy classifier learning

Detection of Social Network Toxic Comments with Usage of Syntactic Dependencies in the Sentences.