David Schoch

Tutorial for Network Visualization in R using ggraph and graphlayouts

Tutorial for Network Analysis in R

Network Analysis in R using tidygraph

The package implements several new layout algorithms to visualize networks which are not provided by standard graph packages. Most are based on the concept of stress majorization. Some more specific algorithms allow to emphasize hidden group structures in networks, focus on specific nodes or specific types of networks.

Implements methods for centrality related analyses of networks. While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilitie.

RStudio addin which provides a GUI to visualize and analyse networks. After finishing a session, the code to produce the plot is inserted in the current script. Alternatively, the function SNAhelperGadget() can be used directly from the console. Additional addins include the Netreader() for reading network files, Netbuilder() to create small networks via point and click, and the Componentlayouter() to layout networks with many components manually.

Centrality indices such as β-centrality, Katz status, and Hubbell’s index are commonly generalized to directed networks by relating the in-centrality of nodes to the in-centrality of their in-neighbors and equivalently so for out-centrality. This paper proposes an extension of Bonacich’s β-centrality…

Online political astroturfing—hidden information campaigns in which a political actor mimics genuine citizen behavior by incentivizing agents to spread information online—has become prevalent on social media. Such inauthentic information campaigns threaten to undermine the Internet’s promise to more equitable participation in public debates. We argue that the logic of social behavior within the campaign bureaucracy and principal–agent problems lead to detectable activity patterns among the…

Multivariate networks comprising several compositional and structural variables can be represented as multigraphs by various forms of aggregations based on vertex attributes. We propose a framework to perform exploratory and confirmatory multiplexity analysis of aggregated multigraphs in order to find relevant associations between vertex and edge attributes…