The problem of predicting the missing links between nodes or the links that may form in the
future in a network is an important task of network analysis. However, the problem is not …

Complex networks are widely used to represent an abundance of real-world relations
ranging from social networks to brain networks. Inferring missing links or predicting future …

Link prediction is a hot research topic in graph data analytics, which aims to identify the
likelihood of a future connection between two nodes in a network. Among the approaches to …

Link prediction is a crucial task in graph analysis that aims to predict the existence of missing
links in a graph. Graph embedding methods have gained popularity for link prediction by …

The link prediction can be used to seek missing or future links in the network, so it has
become a hot research topic. The network generally contains two types of information: the …

Link prediction is a fundamental problem of data science, which usually calls for unfolding
the mechanisms that govern the micro‐dynamics of networks. In this regard, using features …

Link prediction is an important task of graph data mining that facilitates a series of
applications including recommendation system, network reconstruction, etc. Node attributes …

The enclosing subgraph of a target link has been proved to be effective for prediction of
potential links. However, it is still unclear what topological features of the subgraph play the …

Link prediction is one of the most important tasks in graph machine learning, which aims at
predicting whether two nodes in a network have an edge. Real-world graphs typically …

Network embedding aims to learn distributed vector representations of nodes in a network.
The problem of network embedding is fundamentally important. It plays crucial roles in many …