An Influence Spread Blocking Maximization Method based on Node Mixed Blocking Gain

While online social networks make it easier for people to share information, they can also accelerate the spread of unwanted information, which can have a serious impact on society. Therefore, this paper investigates the problem of influence spread blocking maximization, and introduces the concept of mixed blocking gain of nodes, i.e. the ability of nodes to blocking influence spread can be measured by the node’s activation probability and the influence probability propagated to other nodes, and based on this, the mixed blocking gain of the node is estimated. And a heuristic greedy algorithm is designed to select the node with the maximum blocking gain in each iteration and add it to the set of blocking nodes. Finally, it is compared with commonly used algorithms on real social network datasets of different sizes, and the results indicate that the algorithm in this paper has better influence spread blocking and can be applied to large-scale networks.