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Most relationships in a social network have an element of asymmetry: the strength of A's relationship to B need not be the same as B's to A; and relationships that are based on power or influence have a natural flow associated with them. It is therefore natural to model many kinds of social networks by directed graphs, with a node corresponding to each(More)
The relationships within criminal groups are of qualitatively different kinds, and are typically not symmetric because of issues of power and influence. Social network analysis techniques have not been able to model this richness of relationships well. We develop a new technique for spectral embedding of directed graphs, and combine it with a recently(More)
The interactions in real-world social networks change over time. Dynamic social network analysis aims to understand the structures in networks as they evolve, building on static analysis techniques but including variation. Working directly with the graphs that represent social networks is difficult, and it has become common to use spectral techniques that(More)
Most real-world social network analysis treats edges (relationships) as having different intensities (weights), but the same qualitative properties. We address the problem of modelling edges of qualitatively different types that nevertheless interact with one another. For example, influence flows along friend and colleagues edges differently, but treating(More)
Real-world social networks contain relationships of multiple different types, but this richness is often ignored in graph-theoretic modelling. We show how two recently developed spectral embedding techniques, for directed graphs (relationships are asymmetric) and for signed graphs (relationships are both positive and negative), can be combined. This(More)
Dynamic social network analysis aims to understand the structures in networks as they evolve, as nodes appear and disappear, and as edge weights change. Working directly with a social network graph is difficult, and it has become standard to use spectral techniques that embed a graph in a geometry. Analysis can then be done in the geometry where distance(More)
This paper explores the spatial and temporal diffusion of political violence in North and West Africa. It does so by endeavoring to represent the mental landscape that lives in the back of a group leader's mind as he contemplates strategic targeting. We assume that this representation is a combination of the physical geography of the target environment, and(More)
Semi-supervised learning makes the realistic assumptions that labelled data is typically rare, and that unlabelled data that are similar are likely to belong to the same class. Unlabelled data are assigned the labels associated with their “most similar” labelled neighbors. For graph-based semi-supervised learning, “most similar”(More)