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Direction-Aware Proximity on Graphs

Direction-Aware Proximity on Graphs

Hanghang Tong, Yehuda Koren, Christos Faloutsos
Copyright: © 2009 |Pages: 8
ISBN13: 9781605660103|ISBN10: 1605660108|EISBN13: 9781605660110
DOI: 10.4018/978-1-60566-010-3.ch101
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MLA

Tong, Hanghang, et al. "Direction-Aware Proximity on Graphs." Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, IGI Global, 2009, pp. 646-653. https://doi.org/10.4018/978-1-60566-010-3.ch101

APA

Tong, H., Koren, Y., & Faloutsos, C. (2009). Direction-Aware Proximity on Graphs. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 646-653). IGI Global. https://doi.org/10.4018/978-1-60566-010-3.ch101

Chicago

Tong, Hanghang, Yehuda Koren, and Christos Faloutsos. "Direction-Aware Proximity on Graphs." In Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, 646-653. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-010-3.ch101

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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?

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