Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

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Quantum Random Walk via Classical Random Walk With Internal States

In recent years quantum random walks have garnered much interest among quantum information researchers. Part of the reason is the prospect that many hard problems can be solved efficiently by employing algorithms based on quantum random walks, in the same way that classical random walks have played a central role in many hugely successful randomized algorithms. In this paper we introduce a new ...

Community aware random walk for network embedding

Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding r...

Weighted Random Walk on PPI Network

Proteins in the same protein complexes should highly interact with each other but rarely interact with the other proteins in protein-protein interaction (PPI) networks. All interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. Visualization representation of data visually and is an important task in scient...

Link Prediction using Network Embedding based on Global Similarity

Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...