Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"Exponential random graph model"”.
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Expressivity and Structure in Networks: Ising Models, Random Graphs, and Neural Networks.
Networks are used ubiquitously to model global phenomena which emerge due to interactions between multiple agents and are among the objects of fundamental interest in machine learning. The purpose of this dissertation is to understand expressivity and structure in various network models. The basic …
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Recent Advances on Statistical Network Analysis and Multi-task Learning for Complex Data
… clinical trial data. First, I propose a novel exponential random graph model (ERGM) to study the common knowledge (CK) phenomenon in Facebook social networks. Unlike traditional contagion models, CK allows individuals to coordinate their activation as a group, thereby facilitating both the …
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Bayesian multiple-network multi-layer exponential random graph models (MNML-ERGMs): developing efficient inference and application to neuroimaging
… of statistical networks using probabilistic modelling. However, most network models focus on describing a single binary network. The exponential random graph model (ERGM) belongs to such models, which characterise the distribution of networks through a set of network summary statistics. Each …
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Exponential Random Graphs and a Generalization of Parking Functions
<p>Random graphs are a powerful tool in the analysis of modern networks. Exponential random graph models provide a framework that allows one to encode desirable subgraph features directly into the probability measure. Using the theory of graph limits pioneered by Borgs et. al. as a foundation, we …
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The Purpose and Types of Organizational Gossip
… and social network analysis methods such as Exponential Random Graph Model or Triadic Relation Models. We also used qualitative methods as semi-structured interviews and gossip speech interpretation.
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Lifted probabilistic relational inference for uncertain networks
Probabilistic Relational Graphical Model (PRGM) is a popular tool for modeling uncertain relational knowledge, of which the set of uncertain relational knowledge is usually assumed to be independent with the domain of the application. One common application of PRGM is to model complex networks …
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Statistical inference on network data
Networks arise from modeling complex systems in various fields, such as computer science, social science, biology, psychology and finance. Understanding and analyzing networks help us better understand these complex systems and extract useful information. In this dissertation, we study problems on …
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Corporate networks of international investment and trade
… rather production is fragmented into several geographically segmented business functions. This thesis argues the need for both alternative methods and data to better explain complex trade and investment, (and therefore production patterns), in the modern global economy. The first paper advocates …
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The use of exponential random graph models to explore social foraging dynamics of interspecific songbird assemblages
… Statistical advances now allow networks to be modeled, which has expanded the capabilities of network analysis for hypothesis testing. One such advance is exponential random graph models (ERGMs). Developed for the social sciences, ERGMs analyze how network structures (i.e., configurations of …
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ELITES AND SOCIAL MEDIA. EXPLORATORY STUDY ON ELITES' BEHAVIOUR ON SOCIAL MEDIA AND TECHNOCRATIC, TECHNO-POPULIST AND POPULIST ATTITUDES
… the Italian society. We also collect socio-demographic information about these actors, including date and place of birth, gender, level of education, type of education, subject of specialization, position held and institution the actor works for. These allow us to test some confirmatory …