Direction-Aware Proximity on Graphs

View Sample PDF

Author(s): Hanghang Tong (Carnegie Mellon University, USA), Yehuda Koren (AT&T Labs - Research, USA)and Christos Faloutsos (Carnegie Mellon University, USA)
Copyright: 2009
Pages: 8
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch101

Purchase

View Direction-Aware Proximity on Graphs on the publisher's website for pricing and purchasing information.

Abstract

In many graph mining settings, measuring node proximity is a fundamental problem. While most of existing measurements are (implicitly or explicitly) designed for undirected graphs; edge directions in the graph provide a new perspective to proximity measurement: measuring the proximity from A to B; rather than between A and B. (See Figure 1 as an example). In this chapter, we study the role of edge direction in measuring proximity on graphs. To be specific, we will address the following fundamental research questions in the context of direction-aware proximity: 1. Problem definitions: How to define a directionaware proximity? 2. Computational issues: How to compute the proximity score efficiently? 3. Applications: How can direction-aware proximity benefit graph mining?

The IRMA Community

Research IRM

Direction-Aware Proximity on Graphs

Purchase

Abstract

Related Content

IRMA Sponsors