PageRank: Graph Processing Using Dataflow to Rank Web Pages According to Importance

DOI

10.2991/aebmr.k.220405.350How to use a DOI?

Keywords

web graph; resilient distributed datasets; contribution

Abstract

When a huge number of web pages appear, it is difficult for users to find useful information they need. There is a method to solve this problem. It can sort web pages according to their importance. This method is called PageRank in Spark. In this work, we introduced how PageRank works to rank web pages by importance, as well as the purpose and calculation formula of each part of the programming models. What is more, we also discussed the efficiency of PageRank by tackling different sizes of the graph and further applications of PageRank. The conclusion is that PageRank takes less extra time to process larger-size images than it takes to process smaller-size images. In other words, it is efficient for PageRank to tackle large graphs. So, PageRank can be widely used in many aspects.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Ziyu Huang
PY  - 2022
DA  - 2022/04/29
TI  - PageRank: Graph Processing Using Dataflow to Rank Web Pages According to Importance
BT  - Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022)
PB  - Atlantis Press
SP  - 2085
EP  - 2088
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220405.350
DO  - 10.2991/aebmr.k.220405.350
ID  - Huang2022
ER  -