<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order <italic>M</italic> plus <italic>N</italic> log <italic>N</italic>, where <italic>N</italic> is the number of nodes and <italic>M</italic> is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.</p></abstract>

Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order M plus N log N, where N is the number of nodes and M is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.

Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs

Connections

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs"}

Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to...

Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs

Pubblicato online: 17 giu 2020

Pagine: 1 - 24

DOI: https://doi.org/10.21307/connections-2019.009

Parole chiave
Graph analytic triage, Node-neighbor centrality, Standard canonical form for graphs, Comparing graphs

© 2020 Patrick Allen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs

Pubblicato online: 17 giu 2020

Pagine: 1 - 24

DOI: https://doi.org/10.21307/connections-2019.009

Parole chiaveGraph analytic triage, Node-neighbor centrality, Standard canonical form for graphs, Comparing graphs

© 2020 Patrick Allen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Graph analytic triage, Node-neighbor centrality, Standard canonical form for graphs, Comparing graphs