1,720,970 research outputs found
Structure and dynamics of social bipartite and projected networks
No full textNetworks have become ubiquitous across many areas of knowledge. Their popularity comes from the fact that, apart from the variety of the nature of the systems, they present similar architecture governed by universal properties. Moreover, networks function as a skeleton, by mapping the interactions between the elements of the system translated into nodes and links. We can use networks to represent even more complicated systems, e.g. those with elements of two different types. For such cases, we use bipartite networks. Despite their importance for the analysis of complex systems, bipartite networks are often neglected. In general, one-mode versions of the bipartite network are created using the preferred node type. However, such versions— one-mode projected networks — inherently present a loss of information, which would most likely result in impaired analysis. The goal of this thesis is to provide further knowledge about the structure of bipartite networks and, more importantly, how it affects the structural properties of projected networks. First, we show the causality between the degree distributions of bipartite networks and the resulting degree distribution of projected networks. Also, we find that the bipartite degree distributions are not the only feature driving topology formation in projected networks. Thus, we move forward to another network structural feature: small cycles. They represent types of clustering in bipartite networks and directly affect the projected network structure. We use empirical and synthetic networks to show that while four-cycles indicate recurrence of links between a pair of nodes in the projections, six-cycles — representation of transitivity— affect clustering levels. Third, we introduce the dynamics of network growth. We use extensive datasets to study the evolution of the structure of scientific collaboration networks. We create a comprehensive mapping of how several network structural properties evolve over time. Finally, we propose a generative model for bipartite networks. It is a bipartite extension of a model previously designed for one-mode networks. We show that with the proper adaptation, the model can assess the fundamental structural properties that we have studied throughout the thesis, reproducing both bipartite and projected network features
Networks of Confessional Affiliation: Religious Choice and the Schism of Utrecht
Get PDFpeer reviewedThis article demonstrates our methodology for studying the process of intra-Catholic confessional affiliation during the schism that occurred in the Catholic Church in the eighteenth-century Dutch Republic. The schism resulted in the remarkable fact that the Republic, a Protestant state, became home to two competing Catholic Churches, the Roman Catholic Church and the Roomsch-Katholieke Kerk der Oud-Bisschoppelijke Cleresie (OBC Church).
In order to understand the choices made by laypeople for one of the two Catholic Churches, we created a dataset based on the baptisms and marriages taking place in two mission stations that were part of the OBC Church in the cities of Utrecht and Leiden. The data was ingested into a specifically designed graph database – connecting people, events (baptism and marriages), and places (churches and secular courts) – that enabled us to study the people who participated in events in the Catholic Churches as well as their roles at these events. In addition, we constructed two-mode networks, connecting people to events, and their projections, consisting only of links between people. Taken together, this approach allowed us to perform both detailed and structural analysis of the data.
One insight revealed by our analysis is the existence of a group of lay Catholics who participated in events taking place in rival Catholic Churches. Moreover, network analysis has shown that the process of intra-confessional religious affiliation did not take place in the context of larger groups or collectives, but nor was it a strictly individual affair, as it mainly occurred at the level of couples or individual family nuclei.
Our mixed-methods approach, combining qualitative and quantitative analyses, has various advantages as it (1) enhances our understanding of the schism; (2) enables a more detailed analysis of religious choice than the quantitative methodology adopted in the older literature on the schism; and (3) spurs and gives focus to further archival research
Bipartite networks describe R&D collaboration between institutions
No full textA number of real world networks are, in fact, one-mode projections of bipartite networks comprised of two types of nodes. For institutions engaging in collaboration for technological innovation, the underlying network is bipartite with institutions (agents) linked to the patents they have filed (artifacts), while the projection is the co-patenting network. Projected network topology is highly affected by the underlying bipartite structure, hence a lack of understanding of the bipartite network has consequences for the information that might be drawn from the one-mode co-patenting network. Here, we create an empirical bipartite network using data from 2.7 million patents. We project this network onto the agents (institutions) and look at properties of both the bipartite and projected networks that may play a role in knowledge sharing and collaboration. We compare these empirical properties to those of synthetic bipartite networks and their projections in order to understand the processes that might operate in the network formation. A good understanding of the topology is critical for investigating the potential flow of technological knowledge. We show how degree distributions and small cycles affect the topology of the one-mode projected network - specifically degree and clustering distributions, and assortativity. We propose new network based metrics to quantify how collaborative agents are in the co-patenting network. We find that several large corporations that are the most collaborative agents in the network, however such organisations tend to have a low diversity of collaborators. In contrast, the most prolific institutions tend to collaborate relatively little but with a diverse set of collaborators. This indicates that they concentrate the knowledge of their core technical research, while seeking specific complementary knowledge via collaboration with smaller companies
Latent space generative model for bipartite networks
No full textGenerative network models are extremely useful for understanding the mechanisms that operate in network formation and are widely used across several areas of knowledge. However, when it comes to bipartite networks -- a class of network frequently encountered in social systems -- generative models are practically non-existent. Here, we propose a latent space generative model for bipartite networks growing in a hyperbolic plan. It is an extension of a model previously proposed for one-mode networks, based on a maximum entropy approach. We show that, by reproducing bipartite structural properties, such as degree distributions and small cycles, bipartite networks can be better modelled and one-mode projected network properties can be naturally assessed
Degree distributions of bipartite networks and their projections.
No full textpeer reviewedBipartite (two-mode) networks are important in the analysis of social and economic systems as they explicitly show conceptual links between different types of entities. However, applications of such networks often work with a projected (one-mode) version of the original bipartite network. The topology of the projected network, and the dynamics that take place on it, are highly dependent on the degree distributions of the two different node types from the original bipartite structure. To date, the interaction between the degree distributions of bipartite networks and their one-mode projections is well understood for only a few cases, or for networks that satisfy a restrictive set of assumptions. Here we show a broader analysis in order to fill the gap left by previous studies. We use the formalism of generating functions to prove that the degree distributions of both node types in the original bipartite network affect the degree distribution in the projected version. To support our analysis, we simulate several types of synthetic bipartite networks using a configuration model where node degrees are assigned from specific probability distributions, ranging from peaked to heavy-tailed distributions. Our findings show that when projecting a bipartite network onto a particular set of nodes, the degree distribution for the resulting one-mode network follows the distribution of the nodes being projected on to, but only so long as the degree distribution for the opposite set of nodes does not have a heavier tail. Furthermore, we show that bipartite degree distributions are not the only feature driving topology formation of projected networks, in contrast to what is commonly described in the literature
Moderation and authority-building process: the dynamics of knowledge creation on history subreddits
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Networks from archives: Reconstructing networks of official correspondence in the early modern Portuguese empire
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Degree distributions of bipartite networks and their projections.
No full textBipartite (two-mode) networks are important in the analysis of social and economic systems as they explicitly show conceptual links between different types of entities. However, applications of such networks often work with a projected (one-mode) version of the original bipartite network. The topology of the projected network, and the dynamics that take place on it, are highly dependent on the degree distributions of the two different node types from the original bipartite structure. To date, the interaction between the degree distributions of bipartite networks and their one-mode projections is well understood for only a few cases, or for networks that satisfy a restrictive set of assumptions. Here we show a broader analysis in order to fill the gap left by previous studies. We use the formalism of generating functions to prove that the degree distributions of both node types in the original bipartite network affect the degree distribution in the projected version. To support our analysis, we simulate several types of synthetic bipartite networks using a configuration model where node degrees are assigned from specific probability distributions, ranging from peaked to heavy-tailed distributions. Our findings show that when projecting a bipartite network onto a particular set of nodes, the degree distribution for the resulting one-mode network follows the distribution of the nodes being projected on to, but only so long as the degree distribution for the opposite set of nodes does not have a heavier tail. Furthermore, we show that bipartite degree distributions are not the only feature driving topology formation of projected networks, in contrast to what is commonly described in the literature
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