A new influence maximization algorithm based on GC-centrality

The Influence Maximization (IM) problem aims to find a set of seed nodes to maximize the spread of their influence in social networks. This issue has a very important application background in viral marketing and has attracted extensive research in academia and industry. In order to find nodes with more widely influence and core position in the entire network, this paper proposes a new centrality ranking strategy named Influence Maximization Algorithm based on GC-Centrality. Firstly, GC-Centrality is defined to search for the core links of the network, and then the proposed indicators are used to select seed nodes while suppressing their neighbours. Finally, two experiments were conducted on real datasets, indicating that the proposed algorithm is superior to the comparison algorithm.