TY - JOUR
T1 - An On-Line Web Visualization System with Filtering and Clustering Graph Layout
AU - Lai, Wei
AU - Huang, Xiaodi
AU - Wibowo, Ronald
AU - Tanaka, Jiro
N1 - Imported on 12 Apr 2017 - DigiTool details were: Journal title (773t) = IEEE Intelligent Informatics Bulletin. ISSNs: 1727-5997;
PY - 2005
Y1 - 2005
N2 - A Web graph refers to the graph that is used to represent relationships between Web pages in cyberspace, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is a very huge graph as growing with cyberspace. To use it for Web navigation, only a small part of the Web graph is displayed each time according to a user's navigation focus. The graph layout has always been a challenge for visualizing systems. In this paper, we present a visualization system of an online Web graph, together with the methods for clustering and filtering large graphs. In this system, a Web crawler process is used to get on-line information of the Web graph. Filtering and clustering processes reduce the graph complexities on visualization. In particular, the filtering removes those unimportant nodes while the clustering groups a set of highly connected nodes and edges into an abstract node. The visualization process incorporates graph drawing algorithms, layout adjustment methods, as well as filtering and clustering methods in order to decide which part of the Web graph should be displayed and how to display it based on the user's focus in navigation.
AB - A Web graph refers to the graph that is used to represent relationships between Web pages in cyberspace, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is a very huge graph as growing with cyberspace. To use it for Web navigation, only a small part of the Web graph is displayed each time according to a user's navigation focus. The graph layout has always been a challenge for visualizing systems. In this paper, we present a visualization system of an online Web graph, together with the methods for clustering and filtering large graphs. In this system, a Web crawler process is used to get on-line information of the Web graph. Filtering and clustering processes reduce the graph complexities on visualization. In particular, the filtering removes those unimportant nodes while the clustering groups a set of highly connected nodes and edges into an abstract node. The visualization process incorporates graph drawing algorithms, layout adjustment methods, as well as filtering and clustering methods in order to decide which part of the Web graph should be displayed and how to display it based on the user's focus in navigation.
KW - Open access version available
M3 - Article
SN - 1727-5997
VL - 5
SP - 11
EP - 17
JO - IEEE Intelligent Informatics Bulletin
JF - IEEE Intelligent Informatics Bulletin
IS - 1
ER -