LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Influence Maximization Based on Network Motifs in Mobile Social Networks

Photo from wikipedia

A mobile social network (MSN) is a mobile communications system that involves the social relationship of the users, in such a network, mobile users can spread information, opinions, ideas, rumors.… Click to show full abstract

A mobile social network (MSN) is a mobile communications system that involves the social relationship of the users, in such a network, mobile users can spread information, opinions, ideas, rumors. Influence Maximization (IM) aims to identify $k$ nodes from a network such that the influence spread generated by the $k$ nodes is maximized, which has been attracting increasing attention in recent years. However, existing methods of influence maximization are heuristic algorithms based on network topology and greedy algorithms based on spreading. Accordingly, in this paper, we focused on Network Motifs (NM) as drivers of influence to impact the spreading process, we proposed IM-NM, a network motifs-based influence maximation scheme for delivering information efficiently. In consideration of the communication relationship and the users’ attributes, we first defined Weight Ratio (WR), Degree Density (DD), and Structural Stability Level (SSL). Then we identified the key network motifs by Naive Bayesian machine learning. Finally, we adopted the $k$ key network motifs as the unit structure to reconstruct the network, and select the bridge node with strong communication ability in the key motifs to maximize the information. We implement our proposed methods on a set of real-world networks to evaluate the performance, the experimental results demonstrate that our proposal achieves better performance than other related methods.

Keywords: inline formula; network; network motifs; tex math

Journal Title: IEEE Transactions on Network Science and Engineering
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.