  Predicting information cascade popularity is a fundamental problem in social
networks. Capturing temporal attributes and cascade role information (e.g.,
cascade graphs and cascade sequences) is necessary for understanding the
information cascade. Current methods rarely focus on unifying this information
for popularity predictions, which prevents them from effectively modeling the
full properties of cascades to achieve satisfactory prediction performances. In
this paper, we propose an explicit Time embedding based Cascade Attention
Network (TCAN) as a novel popularity prediction architecture for large-scale
information networks. TCAN integrates temporal attributes (i.e., periodicity,
linearity, and non-linear scaling) into node features via a general time
embedding approach (TE), and then employs a cascade graph attention encoder
(CGAT) and a cascade sequence attention encoder (CSAT) to fully learn the
representation of cascade graphs and cascade sequences. We use two real-world
datasets (i.e., Weibo and APS) with tens of thousands of cascade samples to
validate our methods. Experimental results show that TCAN obtains mean
logarithm squared errors of 2.007 and 1.201 and running times of 1.76 hours and
0.15 hours on both datasets, respectively. Furthermore, TCAN outperforms other
representative baselines by 10.4%, 3.8%, and 10.4% in terms of MSLE, MAE, and
R-squared on average while maintaining good interpretability.
