GraphInf: A GCN-based Popularity Prediction System for Short Video Networks

Yuchao Zhang, Pengmiao Li, Zhili Zhang, Chaorui Zhang, Wendong Wang, Yishuang Ning, Bo Lian

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As the emerging entertainment applications, short video platforms, such as Youtube, Kuaishou, quickly dominant the Internet multimedia traffic. The caching problem will surely provide a great reference to network management (e.g., traffic engineering, content delivery). The key to cache is to make precise popularity prediction. However, different from traditional multimedia applications, short video network exposes unique characteristics on popularity prediction due to the explosive video quantity and the mutual impact among these countless videos, making the state-of-the-art solutions invalid. In this paper, we first give an in-depth analysis on 105,231,883 real traces of 12,089,887 videos from Kuaishou Company, to disclose the characteristics of short video network. We then propose a graph convolutional neural-based video popularity prediction algorithm called GraphInf. In particular, GraphInf clusters the countless short videos by region and formulates the problem in a graph-based way, thus addressing the explosive quantity problem. GraphInf further models the influence among these regions with a customized graph convolutional neural (GCN) network, to capture video impact. Experimental results show that GraphInf outperforms the traditional Graph-based methods by 44.7%. We believe such GCN-based popularity prediction would give a strong reference to related areas.

Original languageEnglish (US)
Title of host publicationWeb Services – ICWS 2020 - 27th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Proceedings
EditorsWei-Shinn Ku, Yasuhiko Kanemasa, Mohamed Adel Serhani, Liang-Jie Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030596170
StatePublished - 2020
Event27th International Conference on Web Services, ICWS 2020, held as part of the Services Conference Federation, SCF 2020 - Honolulu, United States
Duration: Sep 18 2020Sep 20 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12406 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Web Services, ICWS 2020, held as part of the Services Conference Federation, SCF 2020
CountryUnited States

Bibliographical note

Funding Information:
The work was supported in part by the National Natural Science Foundation of China (NSFC) Youth Science Foundation under Grant 61802024, the Fundamental Research Funds for the Central Universities under Grant 24820202020RC36, the National Key R&D Program of China under Grant 2019YFB1802603, and the CCF-Tencent Rhinoceros Creative Fund under Grant S2019202.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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