Network embedding aims to embed nodes into a low-dimensional space, while
capturing the network structures and properties. Although quite a few promising
network embedding methods have been proposed, most of them focus on static
networks. In fact, temporal networks, which usually evolve over time in terms
of microscopic and macroscopic dynamics, are ubiquitous. The micro-dynamics
describe the formation process of network structures in a detailed manner,
while the macro-dynamics refer to the evolution pattern of the network scale.
Both micro- and macro-dynamics are the key factors to network evolution;
however, how to elegantly capture both of them for temporal network embedding,
especially macro-dynamics, has not yet been well studied. In this paper, we
propose a novel temporal network embedding method with micro- and
macro-dynamics, named $\rm{M^2DNE}$. Specifically, for micro-dynamics, we
regard the establishments of edges as the occurrences of chronological events
and propose a temporal attention point process to capture the formation process
of network structures in a fine-grained manner. For macro-dynamics, we define a
general dynamics equation parameterized with network embeddings to capture the
inherent evolution pattern and impose constraints in a higher structural level
on network embeddings. Mutual evolutions of micro- and macro-dynamics in a
temporal network alternately affect the process of learning node embeddings.
Extensive experiments on three real-world temporal networks demonstrate that
$\rm{M^2DNE}$ significantly outperforms the state-of-the-arts not only in
traditional tasks, e.g., network reconstruction, but also in temporal
tendency-related tasks, e.g., scale prediction.

本研究提出了一种新的时变网络嵌入方法 M2DNE，它能够精细地捕获网络结构和性质中的微观和宏观动态，大量实验证明 M2DNE 在传统任务和时间倾向相关任务中的表现均显著优于现有技术。