A Graph Temporal Information Learning Framework for Popularity Prediction

被引:4
|
作者
Yang, Caipiao [1 ]
Bao, Peng [1 ]
Yan, Rong [1 ]
Li, Jianian [1 ]
Li, Xuanya [2 ]
机构
[1] Beijing Jiaotong Univ, Beijing, Peoples R China
[2] Baidu Inc, Beijing, Peoples R China
来源
COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION | 2022年
基金
中国国家自然科学基金;
关键词
popularity prediction; dynamic graph representation learning; graph convolutional network;
D O I
10.1145/3487553.3524231
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Effectively predicting the future popularity of online content has important implications in a wide range of areas, including online advertising, user recommendation, and fake news detection. Existing approaches mainly consider the popularity prediction task via path modeling or discrete graph modeling. However, most of them heavily exploit underlying diffusion structural and sequential information, while ignoring the temporal evolution information among different snapshots of cascades. In this paper, we propose a graph temporal information learning framework based on an improved graph convolutional network (GTGCN), which can capture both the temporal information governing the spread of information in a snapshot, and the inherent temporal dependencies among different snapshots. We validate the effectiveness of the GTGCN by applying it on a Sina Weibo dataset in the scenario of predicting retweet cascades. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art approaches.
引用
收藏
页码:239 / 242
页数:4
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