Paper Info
Title
Edge Metrics for Visual Graph Analytics: A Comparative Study
Abstract
Visual graph analytics definitely relies on the use of node and edge metrics to identify salient properties in graphs. Most often, these metrics are turned into useful visual cues, or are used to interactively filter out parts of a graph while querying it, for instance. Along the years, analysts coming from different application domains have designed metrics to serve specific needs. Graph analytics, sometimes also called network science, recently developed as a cross-discipline field developing models shared by numerous application domains such as bio-informatics, social network analysis, web graphs, etc.[4] [10]. As a consequence, we end up finding various metrics in the literature aiming at similar goals; different names and analytics description often hide similarity between two metrics that originated from different fields. We survey a list of edge metrics for graphs and compare their relative value and behaviour, in an effort to organize them into a taxonomy and underline the genuine ingredients in each of them disregarding their origin.
Year
DOI
Venue
2008
10.1109/IV.2008.10
IV
Keywords
Field
DocType
data visualisation,graph theory,network theory (graphs),data visualisation,edge metrics,network science,visual graph analytics,edge metrics,survey,visual graph analytics
Graph theory,Sensory cue,Network science,Data visualization,Computer science,Social network analysis,Theoretical computer science,Analytics,Graph (abstract data type),Salient
Conference
ISSN
Citations 
PageRank 
1550-6037
11
0.87
References 
Authors
7
2
Name
Order
Citations
PageRank
Guy Melançon1107672.53
Arnaud Sallaberry214714.86