Compound-fisheye views are introduced as a method for the display and interaction with large graphs. The method relies on a hierarchical clustering of the graph, and a generalization of the traditional fisheye view, together with a treemap representation of the cluster tree.

1 Introduction 324 2 Data and methods 335 2.1 Grouping causes of death 335 2.2 Hierarchical cluster analysis 336 2.2.1 Period-specific geographical patterns 338 2.2.2 Timescale overall geographical patterns 339 3 To each period its own geographical pattern of causes of death

Two tasks in Graph Visualization require partitioning: the assignment of visual attributes and divisive clustering. Often, we would like to assign a color or other visual attributes to a node or edge that indicates an associated value. In an application involving divisive clustering, we would like to partition the graph into subsets of graph elements based on metric values in such a way that al...

We propose a framework based on Hierarchical Clustering (HC) to perform multiagent negotiations where we can specify the type of agreements needed in terms of utility sharing among the agents.The proposed multi-round mediation process is based on the analysis of the agents’ offers at each negotiation round and the generation of a social contract at each round as a feedback to the agents, which ...

Russia has been experiencing a demographic crisis since the 1990s. The most obvious manifestations include an excess of mortality over fertility rates, population decline and an ageing population. The last 20 years have seen considerable activity to come up with new demographic policy measures to mitigate these adverse trends, with single solutions developed for all regions in Russia. This pape...

This paper considers metric spaces where distances between a pair of nodes are represented by distance intervals. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a resolution parameter, induced from the given distance intervals of the metric spaces. Our construction of hierarchical clustering methods is based on definin...

A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The numb...

Population-based HIV surveillance is crucial to inform understanding of the HIV pandemic and evaluate HIV interventions, but little is known about longitudinal participation patterns in such settings. We investigated the dynamics of longitudinal participation patterns in a high HIV prevalence surveillance setting in rural South Africa between 2003 and 2012, taking into account demographic dynam...

In this work we develop a theory of hierarchical clustering for graphs. Our modelling assumption is that graphs are sampled from a graphon, which is a powerful and general model for generating graphs and analyzing large networks. Graphons are a far richer class of graph models than stochastic blockmodels, the primary setting for recent progress in the statistical theory of graph clustering. We ...

Twitter has become as much of a news media as a social network, and much research has turned to analyzing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [Pap14]. We show that aggressive filtering of tweets based on length and structure, combined with hierarc...