Leadership emergence is best conceptualized as a complex, multi-level process arising from the dynamic interplay of all elements in the process: group members, relations, and context (Day, 2014). This study seeks to simultaneously examine to the role of each in the leadership emergence process by assessing leader and follower traits, their trait similarity, task, behaviors, and the network itself. Using a rotation design, 99 cadets in groups of three completed four tasks with alternating partners and subsequently provided sociometric ratings of each of their group members. Data was analyzed using Exponential Random Graph Modeling, which controls for endogenous group effects. In general, there was a tendency toward nominating others as leaders. High scores on dominance and intelligence predicted leadership emergence, and low scores on dominance predicted follower emergence. The type of task did not affect leadership emergence. Perceived leader behavior unexpectedly reduced the likelihood of nominating another as a leader. Results from this study highlight the importance of studying all components of leadership process and are once step closer toward doing so completely and accurately. / Master of Science

L’objectif de cette thèse est de répondre à trois questions principales ; commentexpliquer et interpréter un réseau de collaboration, est-ce que des firmes avec une positionparticulière dans un réseau bénéficient d’une performance accrue et enfin, existe-t-il desstructures de réseaux qui favorisent l’innovation ?Pour répondre à ces questions, la thèse est organisée en trois parties. La première partieprésente, dans un premier chapitre, une revue analytique de la littérature suivie d’un chapitrequi présente la théorie derrière une des méthodes d’analyse réseau utilisée dans cette thèse :les Exponential Random Graph Models (ERGM).La seconde partie présente trois analyses empiriques. Le premier chapitre empirique analysel’impact du cycle de vie de la technologie sur la dynamique du réseau de collaboration autourdes composites structuraux en aéronautique. Les deux chapitres suivants se concentrent sursecteur aéronautique et le secteur des biotechnologies respectivement. L’objectif de ceschapitres est d’analyser la dynamique structurelle et d’identifier s’il existe un lien entreposition dans le réseau et la performance de la firme.La dernière partie cherche à identifier des structures de réseaux qui favorisent l’innovation.Un modèle à base d’agents (ABM) est proposé pour répondre à cette question. / This thesis aims to answer three main questions ; how can one explain andinterpret the structure of an innovation network, are there positions in a network which allowfor an increased performance for firms and finally, are there network structures which favourinnovation ? In order to answer these questions, the thesis is organised in three parts.The first part presents, in a first chapter, an analytical review of the literature followed by achapter presenting the theory behind one of the network analysis methods: ExponentialRandom Graph Models (ERGM).The second part of the thesis presents three empirical analyses. The first empirical chapteranalyses the impact of the life-cycle of the technology on the structural dynamics of thecollaboration network for Structural Composite Materials. The following two chapters focuson two sectors, the aerospace and biotech sector. The aim of these chapters is to analyse thestructural dynamics of collaboration networks as well as identifying a link between networkposition and firm performance.The third and final part of this thesis searches for network structures which might favourinnovation. An Agent-Based Model is used to answer this final question.

Thesis advisor: Thomas M. Crea / The ability of school communities to develop successful integration strategies for youth from immigrant communities is of pressing concern. The goal of this dissertation is to explore how immigrant youth interact with their peers in friendship networks and school communities in order to inform efforts to promote the successful integration of immigrant youth in US schools. Data from the 1994-1995 National Longitudinal Study of Adolescent to Adult Health (Add Health) are used in three separate studies to examine processes of integration. First, the integration of immigrant youth is analyzed at dyadic, network, and school institution levels. Second, exponential random graph modeling (ERGM) is used to examine how immigrant generation plays a role in friendship formation in 63 US schools. Third, cross-sectional ERGM and longitudinal stochastic actor-based models (SABM) are developed to examine how race, immigrant generation, spoken language, and social network processes give rise to youth friendship networks in one US school. Key findings are as follows: first, youth from immigrant families overall are successfully integrating into school friendship networks and communities. First-generation youth are located only slightly on the margins while second-generation youth are located in positions of social advantage. Second, school contexts change the nature of friendship decision-making. Immigrant youth in more diverse schools are more likely to integrate through cross-group friendships, providing evidence for contact theory of intergroup relations. Third, while immigrant generation and spoken language emerge as salient predictors of friendship formation, other factors such as grade level, race and ethnicity, as well as social network processes remain the primary drivers of friendship formation. In the final chapter, an applied theory of immigrant integration in school settings grounded in theories of social structure is proposed. Together, the findings of this research will inform efforts to better serve culturally and linguistically diverse youth in American schools and aim to help promote the integration of youth from immigrant communities. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Graduate School of Social Work. / Discipline: Social Work.

The heightened research activity in the interdisciplinary field of network science can be attributed to the emergence of the social network computer applications. Researchers understood early on that data describing how entities interconnect is highly valuable and that it offers a deeper understanding about the entities themselves. This is why there were so many studies done about various kinds of networks in the last 10-15 years. The study of the networks from the perspective of computer science usually has two objectives. The first objective is to develop statistical mechanisms capable of accurately describing and modeling observed real-world networks. A good fit of such mechanism suggests the correctness of the model's assumptions and leads to better understanding of the network. A second goal is more practical, a well performing model can be used to predict what will happen to the network in the future. Also, such model can be leveraged to use the information gleaned from network to predict what will happen to the networks entities. One important leitmotif of network research and analysis is wide adaptation of log linear models. In this work we apply this philosophy for study and evaluation of log-linear statistical models in various types of networks. We begin with proposal of the new Temporal Exponential Random Graph Model (tERGM) for the analysis and predictions in the binary temporal social networks. We then extended the model for applications in partially observed networks that change over time. Lastly, we generalize the tERGM model to predict the real-valued weighted links in the temporal non-social networks. The log-linear models are not limited to networks that change over time but can also be applied to networks that are static. One such static network is a social network composed of patients undergoing hemodialysis. Hemodialysis is prescribed to people suffering from the end stage renal disease; the treatment necessitates the attendance, on non-changing schedule, of the hemodialysis clinic for a prolonged time period and this is how the social ties are formed. The new log-linear Social Latent Vectors (SLV) model was applied to study such static social networks. The results obtained from SLV experiments suggest that social relationships formed by patients bear influence on individual patients clinical outcome. The study demonstrates how social network analysis can be applied to better understand the network constituents. / Computer and Information Science

This research applied the social network approach to unveil the social structure underlying the members of two traditional irrigation systems (TISs) in Sonora. This research used two TIS case studies representing rural communities located in arid and semiarid lands in the Sonoran Desert region, in the northwestern part of Mexico. The irrigators represented a subset of rural villages where everyone knew everyone else. The theoretical framework in this study suggested that social embeddedness of the economic activities of TIS irrigators is an important factor supporting their local institutions. Irrigators who are socially embedded posses more social capital that help them in overcoming social dilemmas. Evidence of social embeddedness is theoretically incomplete when not related to a tangible dimension of the TIS's performance. This research also dealt with the difficulty of assessing the sustainability or successfulness of a TIS. The results showed that the irrigators sharing a rural village are entangled in a mesh of social ties developed in different social settings. The most salient variable was family; cooperative ties within the irrigation system tend to overlap more than the expected by chance with kinship relationships. Likewise, irrigators had a strong preference for peers geographically close or those within the same irrigation subsector. Finally, the qualitative part of the study did not reveal the presence of severe social dilemmas. Irrigators in each community have developed successful forms of local arrangements to overcome the provision and appropriation issues typical of common pool resources. Nevertheless, the qualitative analysis revealed that there are other socioeconomic variables undermining the sustainability of the systems, such as migration, water shortages and social capacity of the systems.

La littérature économique attribuant des rôles endogènes à l'évolution technologique et à lagéographie pour expliquer la croissance économique suggère que la compréhension des fluxde connaissances dans l'espace et de leurs conséquences sur les activités d'innovation est aucoeur de l'explication des disparités dans la croissance économique. À cet égard, cette thèsemet l'accent sur les réseaux de connaissances comme un mécanisme permettant la circulationdes connaissances dans l'espace et le temps. Parmi les différents types de réseaux deconnaissances, elle étudie les réseaux de collaboration de R&D et explore deux questionsprincipales.Tout d'abord, elle examine l'effet de la géographie sur la formation du réseau pour savoir si lesflux de connaissances par les réseaux de collaboration en R&D sont limités dans l'espace oupas. Elle s'enquiert de cette question à la fois pour le réseau multilatéral entre lesorganisations et le réseau inter-régional entre les régions européennes dans le domaine desbiotechnologies en utilisant les données sur les collaborations de R&D promues par lesProgrammes-Cadres Européens.Deuxièmement, elle explore comment un réseau évolutif de collaborations de R&D entre lesrégions affecte les performances des régions en matière d'innovation. Elle utilise l'économétriespatiale pour quantifier les effets statiques et dynamiques des flux de connaissances desvoisins spatiaux et d'un ensemble évolutif de partenaires de collaboration sur l'activitéinventive des régions dans le domaine des TIC au cours de la période 2003-2009. / The economic literature attributing endogenous roles to technological change and geography inexplaining economie growth suggests that understanding knowledge flows in space and theirconsequences on innovative activities is central to explaining disparities in economie growth. Inthis regard, this PhD thesis focuses on knowledge networks as a mechanism that enablesknowledge flows in space and time. Among different types of knowledge networks, it studiesR&D collaboration networks and addresses two main issues.First, it investigates the effect of geography on network formation to figure out whetherknowledge flows through R&D collaboration networks are constrained in space or not. It inquiresthis question both for multilateral R&D collaboration network among organizations and theresulting inter-regional network among European regions in the field of biotechnology using dataon R&D collaborations promoted via European Framework Programmes.Second, it addresses how an evolving network of R&D collaborations among regions affectsregional innovation performances in time. It employs a spatial econometric approach to quantifythe static and dynamic effects of knowledge flows from spatial neighbors and an evolving set ofcollaboration partners on inventive activity of regions in the field of ICT durin 2003-2009.

How are arms export choices made within a state? In this dissertation I use a foreign policy analysis framework to examine this question. I focus on examining each of the three primary levels of analysis in international relations as it relates to the main question. I begin with a typical international relations level and examine the characteristics of the two states that dominate the world arms trade: The United States and Russia. I then examine the full network of relations among all states in the international system that are involved in the sale or purchase of arms. To do this I use an Exponential Random Graph Model (ERGM) to examine these relations, which I derived from data on arms sales from the Stockholm International Peace Research Institute (SIPRI). I examine the arms sales in each decade from 1950 through 2010. In order to answer the question of how arms decisions are made within the state, I focus my inquiry on the United States and Russia. It is these states that have the practical capability to use arms transfers as a foreign policy tool. I examine the foreign policy making mechanisms in each of these states to determine how arms transfers can be used as a foreign policy tool. I examine and the bureaucratic institutions within each state and come up with a state ordering preference for how arms decisions are evaluated in each state. Finally, I use case studies to examine arms relations between the both the U.S. and Russia and three other states in each case. The other states were selected based on the pattern of sales between the two countries. I examine these sales to determine the impact of bureaucratic maneuvering and interest politics on the decision-making process within Russia and the United States. I find in my network analysis that the traditional measures of state power - military spending, regime type, and military alliances - do not account for the overall structure of the arms sale network. The most important factors in the formation of the arms sale network in each of the six decades that I study are specific configurations of triadic relations that suggest a continued hierarchy in the arms sale network. I find in my case study chapters that a simple model of state interest as a decision-making rule accounts for the decisions made by the different bureaucratic actors in the U.S. Russian arms sales are driven by a state imperative to increase Russia's market share, and there is high-level involvement in making different arms deals with other countries.

Η παρούσα διπλωματική εργασία βρίσκεται στον ευρύτερο χώρο της μαθηματικής στατιστικής θεωρίας των γραφημάτων. Κύριος στόχος μας, όπως αναφέρει και ο τίτλος, είναι η μοντελοποίηση γραφημάτων, με απώτερο σκοπό την προσαρμογή, προσομοίωση και διάγνωση αυτών μέσω μοντέλων εκθετικών τυχαίων γραφημάτων. Το πρώτο κεφάλαιο δίνει μια συνοπτική παρουσίαση της διατύπωσης του προβλήματος και της θεωρίας των μοντέλων των εκθετικών τυχαίων γραφημάτων. Η βασική ιδέα είναι να θεωρήσουμε ως τυχαίες μεταβλητές τους δυνατούς δεσμούς μεταξύ των κόμβων ενός δοθέντος γραφήματος. Η γενική μορφή ενός μοντέλου εκθετικά τυχαίου γραφήματος καθορίζεται από κάποιες υποθέσεις σχετικές με τις εξαρτήσεις μεταξύ αυτών των τυχαίων μεταβλητών. Παρουσιάζουμε κάποιες διαφορετικές υποθέσεις εξάρτησης και τα αντίστοιχα μοντέλα, όπως τα γραφημάτα Bernoulli, τα δυαδικώς - ανεξάρτητα και τα τυχαία γραφήματα Markov. Επίσης, εξετάζουμε την ενσωμάτωση των χαρακτηριστικών, που μπορούν να έχουν οι κόμβοι, σε μοντέλα κοινωνικής επιλογής, δηλαδή, σε περιπτώσεις που οι συνδέσεις του γραφήματος μπορούν να προβλέψουν τα χαρακτηριστικά των κόμβων. Συνοψίζουμε κάποιες καινούργιες υποθέσεις εξάρτησης, που είναι πολυπλοκότερες των πρώτων τέτοιων υποθέσεων της σχετικής βιβλιογραφίας. Συζητούμε τις διαδικασίες της στατιστικής εκτίμησης, συμπεριλαμβανομένων των νέων μεθόδων για την εκτίμηση της μέγιστης πιθανοφάνειας Monte Carlo. Τέλος, παρουσιάζουμε τις νέες προδιαγραφές για μοντέλα εκθετικών τυχαίων γραφημάτων, που έχουν προτείνει οι Snijders et al., οι οποίες βελτιώνουν σημαντικά τα αποτελέσματα της προσαρμογής εμπειρικών δεδομένων για εκθετικά μοντέλα ομοιογενών τυχαίων γραφημάτων Markov. Επιπλέον, οι νέες αυτές προδιαγραφές μας βοηθούν να αποφύγουμε το πρόβλημα του σχεδόν-εκφυλισμού, που συχνά παρεμβάλλεται στη διαδικασία της προσαρμογής μοντέλων εκθετικών τυχαίων γραφημάτων Markov, ιδιαίτερα όταν αυτά προέρχονται από εμπειρικά δεδομένα, που έχουν υψηλό βαθμό μεταβατικότητας. Η μελέτη μιας τέτοιας νέας στατιστικής με υψηλότερης τάξης μεταβατικότητα επιτρέπει την εκτίμηση των παραμέτρων των μοντέλων των εκθετικών γραφημάτων σε πολλές (αλλά όχι όλες) περιπτώσεις, στις οποίες διαφορετικά θα ήταν αδύνατο να εκτιμηθούν οι παράμετροι των μοντέλων των ομοιογενών γραφημάτων Markov. Στο δεύτερο, τρίτο και τέταρτο κεφάλαιο της εργασίας εφαρμόζουμε τις παραπάνω μεθόδους, αντιστοίχως, για τρείς αναλύσεις εμπειρικών δεδομένων: το δίκτυο Florentine, το δίκτυο Faux Magnolia High και τα δίκτυα IPRED και SWPAT. Σε αυτά τα κεφάλαια, παρουσιάζουμε τις διαδικασίες της προσαρμογής, προσομοίωσης και διάγνωσης με παράθεση των αντίστοιχων εντολών, χρησιμοποιώντας τα πακέτα statnet - ermg και sna, τα οποία δουλεύουν στο περιβάλλον του πακέτου ελεύθερου λογισμικού R. Τέλος, στο παράρτημα της εργασίας δίνουμε μια σύντομη εισαγωγή στο περιβάλλον R και σε κάποιες γενικές εντολές αυτού. / This specific project has to do with mathematical statistical graph theory. Our main target is to fit, simulate and diagnose models through exponential random graph models. In the first chapter we give a short presentation of the problem and the theory of exponential random graph models. The main idea is to consider each tie of a given network (graph) as a random variable. The general form of an exponential random graph model is defined from some relative assumptions that have to do with the dependence between those random variables. We present some different dependence assumptions and the corresponding models, such as Bernoulli graphs, dyadic-independent and Markov random graphs. We also examine the incorporation of the characteristics that a node may have in social networks. We also discuss the process of statistical estimation, including three new methods for the estimation of Monte Carlo maximum likelihood. Finally, we present new specifications for exponential random graph models, which Snijders et al. have proposed. These new specifications allow us to avoid the problem of degeneration. In the second, third and fourth chapter we apply the above methods in order to analyze Florentine network data, Faux Magnolia High data and IPred And Swpat data. In those chapters, we present the procedures of fit, simulate and diagnose exponential random graph models displaying the corresponding commands of statnet-ergm and sna packages that work in R. Finally we give a short introduction to R and to some relative commands.