As an effective way of learning node representations in networks, network embedding has
attracted increasing research interests recently. Most existing approaches use shallow …

Recently, Masud et al.[MA Masud, JZ Huang, CH Wei, et al. I-nice: A new approach for
identifying the number of clusters and initial cluster centres. Information Sciences 466 (2018) …

Network embedding or network representation learning aims at learning a low-dimensional
vector for each node in a network. The learned embeddings could advance various learning …

Diffusion processes over networks are widely encountered in various real-world
applications. In many scenarios, however, only the states of nodes can be observed while …

Network representation learning aims at learning a low-dimensional vector for each node in
a network, which has attracted increasing research interests recently. However, most …

Network embedding, as a promising way of node representation learning, is capable of
supporting various downstream network mining tasks, and has attracted growing research …

The fast growth of social networks and their data access limitations in recent years has led to
increasing difficulty in obtaining the complete topology of these networks. However, diffusion …

Network embedding, as a promising way of the network representation learning, is capable
of supporting various subsequent network mining and analysis tasks, and has attracted …

Traditionally, community detection and network embedding are two separate tasks. Network
embedding aims to output a vector representation for each node in the network, and …

In the research process of robustness refinement solution of built‐in information systems for
electronic networks, there are too many factors related to robustness in the current system …