Link prediction in complex networks has attracted considerable attention from
interdisciplinary research communities, due to its ubiquitous applications in
biological networks, social networks, transportation networks,
telecommunication networks, and, recently, knowledge graphs. Numerous studies
utilized link prediction approaches in order sto find missing links or predict
the likelihood of future links as well as employed for reconstruction networks,
recommender systems, privacy control, etc. This work presents an extensive
review of state-of-art methods and algorithms proposed on this subject and
categorizes them into four main categories: similarity-based methods,
probabilistic methods, relational models, and learning-based methods.
Additionally, a collection of network data sets has been presented in this
paper, which can be used in order to study link prediction. We conclude this
study with a discussion of recent developments and future research directions.

本文对复杂网络中的链接预测方法进行了广泛的综述，将其分为四大类，包括基于相似性、基于概率、基于关系模型和基于学习的方法。此外，还介绍了一系列可用于研究链接预测的网络数据集。最后，讨论了最近的发展趋势和未来研究方向。

学习和提取图特征进行链接预测的综述

Review on Learning and Extracting Graph Features for Link Prediction

Many networks of interest in the sciences, including a variety of social and
biological networks, are found to divide naturally into communities or modules.
The problem of detecting and characterizing this community structure has
attracted considerable recent attention. One of the most sensitive detection
methods is optimization of the quality function known as "modularity" over the
possible divisions of a network, but direct application of this method using,
for instance, simulated annealing is computationally costly. Here we show that
the modularity can be reformulated in terms of the eigenvectors of a new
characteristic matrix for the network, which we call the modularity matrix, and
that this reformulation leads to a spectral algorithm for community detection
that returns results of better quality than competing methods in noticeably
shorter running times. We demonstrate the algorithm with applications to
several network data sets.

通过改进算法的方式，基于特征矩阵的谱算法检测网络数据的社区结构，运行时间比模拟退火等竞争方法更短且获得更好质量的结果。