Tanulmányomban a hálózatkutatás eszköztárával tárom fel Budapesti Értéktőzsdén 2020-ban kibocsátóként jelen lévő entitások tulajdonviszonyainak hálózatát, statikus módszerek, valamint exponential random graph modeling (ERGM) elemzés alapján. A hálózat pillanatkép-tipológiája és szimuláció alapú megragadása során nem pusztán tőzsdén levő kibocsátók közötti kapcsolathálózat kerül elemzésre, hanem...

Markov chain Monte Carlo methods can be used to approximate the intractable normalizing constants that arise in likelihood calculations for many exponential family random graph models for networks. However, in practice, the resulting approximations degrade as parameter values move away from the value used to define the Markov chain. Here, we introduce a method of moving toward a maximum likelih...

Short Abstract: We explore the network coverage of a Respondent Driven Sampling (RDS) sample of female sex workers (FSW) in China as part of an effort to evaluate RDS' claim of population representation with empirical data. We take advantage of unique information on the social networks of FSW obtained from two overlapping studies of FSWs --RDS and a venuebased sampling approach (PLACE) -and use...

In structural brain networks the connections of interest consist of white-matter fibre bundles between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion MRI in combination with probabilistic tractography. Unfortunately, as of yet no approaches have been suggested that provide an undisputed way of inferring brai...

Weight-based stigma compromises the social networks of overweight children. To date, research on the position of overweight children in their peer network has focused only on friendship relations, and not on negative relationship dimensions. This study examined how overweight was associated with relations of friendship and dislike (antipathies) in the peer group. Exponential random graph models...

Exponential Random Graphs Models (ERGM) are common, simple statistical models for social network and other network structures. Unfortunately, inference and learning with them is hard even for small networks because their partition functions are intractable for precise computation. In this paper, we introduce a new quadratic time deterministic approximation to these partition functions. Our main...

This paper describes an empirical comparison of four specifications of the exponential family of random graph models (ERGM), distinguished by model specification (dyadic independence, Markov, partial conditional dependence) and, for the Markov model, by estimation method (Maximum Pseudolikelihood, Maximum Likelihood). This was done by reanalyzing 102 student networks in 57 junior high school cl...

Modeling of dynamic networks — networks that evolve over time — has manifold applications in many fields. In epidemiology in particular, there is a need for data-driven modeling of human sexual relationship networks for the purpose of modeling and simulation of the spread of sexually transmitted disease. Dynamic network data about such networks are extremely difficult to collect, however, and m...

Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or ab...

Abstract Attention to informal communication networks within public organizations has grown in recent decades. While research documented the role of individual cognition and social structure understanding information search organizations, this article emphasizes importance formal hierarchy. We argue that structural attributes bureaucracies are too important be neglected when modeling knowledge ...