Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided

Community Structures in Bipartite Networks: A Dual-Projection Approach

Identifying communities or clusters in networked systems has received much attention across the physical and social sciences. Most of this work focuses on single layer or one-mode networks, including social networks between people or hyperlinks between websites. Multilayer or multi-mode networks, such as affiliation networks linking people to organizations, receive much less attention in this literature. Common strategies for discovering the community structure of multi-mode networks identify the communities of each mode simultaneously. Here I show that this combined approach is ineffective at discovering community structures when there are an unequal number of communities between the modes of a multi-mode network. I propose a dual-projection alternative for detecting communities in multi-mode networks that overcomes this shortcoming. The evaluation of synthetic networks with known community structures reveals that the dualprojection approach outperforms the combined approach when there are a different number of communities in the various modes. At the same time, results show that the dual-projection approach is as effective as the combined strategy when the number of communities is the same between the modes

Impact of Board Dynamics in Corporate Bankruptcy Prediction: Application of Temporal Snapshots of Networks of Board Members and Companies

Firma pankrott mõjutab erinevaid ettevõttega seotud huvigruppe, näiteks investoreid, võlausaldajad, konkurente, töötajad, ja seetõttu on pankroti ennustamise vastu tõsine majanduslik huvi. Kuigi seda probleemi on juba laialdaselt uuritud, on enamasti ennustuste tegemiseks kasutatud ettevõtete varasemaid finantsandmeid. Kuna majandusaasta aruanded koostatakse ja avalikustatakse alles peale majandusaasta lõppu, ei ole ennustused enam ajakohased. Samal ajal avalikustatakse juhatuse liikmete muudatused ilma erilise viivituseta. Antud töö uurib, kas juhatuse liikmete ja firmade graafi võrgumeetrikad mõjutavad ennustuste täpsust ning seeläbi muudaks ennustused ajakohasemaks. Töös tehtud eksperimentide tulemused näitavad, et võrgumeetrikad, eriti PageRank, degree ja eccentricity, suurendavad mudelite täpsust. Parimaks mudeliks osutus otsustuspuul põhinev random forests, mis suutis pankrotti klassifitseerida kuni üheksa kuud ette.Corporate bankruptcy affects significantly a variety of stakeholders, such as investors, creditors, competitors, employees, and is therefore an event, in which there is a serious economic interest to predict it well ahead. Although this topic is widely studied, typically annual financial data is used to make predictions. However, due to significant delay in publication of such data, the predictions are often outdated. At the same time, changes in board membership of companies are made public with significantly shorter delay. This thesis investigates whether usage of network metrics of networks of board members and companies will positively impact accuracy and timeliness of bankruptcy prediction. More specifically, the thesis reveals that network metrics, especially PageRank, degree and eccentricity, indeed improve bankruptcy prediction models. Furthermore, by using random forest learning method and network metrics, the author was able to construct a classification model that was capable of predicting bankruptcy up to nine months in advance