Community-Based Influence Maximization Using Network Embedding in Dynamic Heterogeneous Social Networks

Influence maximization (IM) is a very important issue in social network diffusion analysis. The topology of real social network is large-scale, dynamic, and heterogeneous. The heterogeneity, and continuous expansion and evolution of social network pose a challenge to find influential users. Existing IM algorithms usually assume that social...

Influence of clustering coefficient on network embedding in link prediction

Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely to perform better given the topological properties of the network. In this paper, we...

Susceptible-infected-spreading-based network embedding in static and temporal networks

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task....

Degree-biased random walk for large-scale network embedding

Network embedding aims at learning node representation by preserving the network topology. Previous embedding methods do not scale for large real-world networks which usually contain millions of nodes. They generally adopt a one-size-fits-all strategy to collect information, resulting in a large amount of redundancy. In this paper, we propose...