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“Niche Work” Graham J Wills, Lucent Technologies (Bell Lab)

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Presentation on theme: "“Niche Work” Graham J Wills, Lucent Technologies (Bell Lab)"— Presentation transcript:

1 “Niche Work” Graham J Wills, Lucent Technologies (Bell Lab)

2 Why Niche Works? used to visualize weighted networks using an interactive linked views environment

3 How does “Niche work” works? Data definition stage initial layout algorithm –map data variables to node/edge attributes Iterative Algorithm –improve the layout

4 Types of initial algorithms Circular Layout –nodes are placed at random on the periphery of a single –circle Hexagonal Grid –nodes are placed at random on the grid points of a –regular hexagonal grid Tree Layout –nodes are placed with the root node in the center

5 Types of incremental algorithms summation of (1-dw)^2 and abs(1-dw) –d= edge length and w = edge weight Steepest Descent –Potential of the graph is a function of the 2Ndimensional vector of location of its nodes Simulated Annealing Swapping Algorithm –calculates the difference in potential if the nodes were swapped Repelling Algorithm –Calculates the nearest neighbors for all nodes and moves the closest ones apart a small distance.

6 Features of Niche Works Used to show or hide parts of a graph via interactive manipulation. Linked views of graph and statistics Rapid layout capability for large graphs Multiple layout methods and display options

7 Web site with nodes of type”link” highlighted

8 Web sites of the Chicago Tribune

9 Areas of application Relationship between function and files in a large software development effort. –understand functional relationships between parts create links by examining modification history Web site analysis – navigation – understanding how the web site is laid out To examine large telecommunication networks Correlation analysis in large data bases –establishing functional relationship between variables

10 Future work Networks with Hierarchies Time series –when edges were created, measuring evolution and structure changes in graphs Additional Layout Algorithms Parallel Implementations

11 HCI Metrics Learning Curve Ease of use Error recovery User satisfaction Retention


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