A nested-graph model for the representation and manipulation of complex objects
ACM Transactions on Information Systems (TOIS)
Self-Organizing Maps
Constructing and reconstructing the reorderable matrix
Information Visualization
Graphs and Hypergraphs
MatrixExplorer: a Dual-Representation System to Explore Social Networks
IEEE Transactions on Visualization and Computer Graphics
Survey of graph database models
ACM Computing Surveys (CSUR)
Annual Review of Information Science and Technology
Clustering graphs for visualization via node similarities
Journal of Visual Languages and Computing
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In the literature several authors describe methods to construct simplified models of networks. These methods are motivated by the need to gain insight into the main properties of medium sized or large networks. The present paper contributes to this research by setting focus on weighted networks, the geographical component of networks and introducing a class of functions to model how the weights propagate from one level of abstraction to the next. Hierarchies of network models can be constructed from reordering of the adjacency matrix of the network; this is how ''hypernodes'' are derived in the present paper. The hypernode algorithm is explored and it is shown how it can be formulated to handle weighted networks. Weighted networks enable handling the uncertainty or the strength of the components which make up the network. The hypernode algorithm can be run in an iterative manner so that a hierarchy of simplified models of the network can be derived. Some case studies demonstrate the hypernode algorithm. In the first case the algorithm is compared with a similar implementation described in the literature. In the second case an airline dataset is analysed. This study shows that when networks are embedded in the geographical space hypernodes may relate to clusters in the spatial domain. The selection of the visual variables to illustrate the strength of the edges and nodes in a weighted network is discussed.