Improved CVoteRank: A Learning‐Based Community Voting Framework for Influence Maximization

ABSTRACT

One of the main challenges with influence maximization on complex networks is identifying a small yet effective set of influential spreaders. Current centrality‐ and voting‐based techniques tend to choose nodes within the same dense parts, resulting in overlapping influence and low spreads. Additionally, they are based on predetermined heuristics and cannot adjust to various network structures or historic diffusion patterns. To solve these problems, we introduce Improved CVoteRank, a learning and community‐aware instructional structure of voting that employs the information given by earlier states of the network to inform seed selection. The approach is a concatenation of several structural characteristics that symbolize both global and local spreading capacity, such as size‐sensitive Kullback‐Leibler divergence, participation coefficient, and intra‐community betweenness centrality. The ridge regression model is trained on the previous results of the diffusion and is automatically trained to find the most appropriate significance of the features. The resultant learned weights are used in a refined iterative voting process using concave neighbor influence and two‐hop suppression to minimize redundancy and enhance seed diversity. Experimental results on seven real networks in social, collaboration, infrastructure, and peer‐to‐peer systems indicate that Improved CVoteRank can always achieve faster information spreading and larger ultimate outbreak sizes of the Susceptible‐Infected‐Recovered (SIR) model. The proposed approach also shows a high level of robustness when the seed budgets and rates of infection vary. Structural analysis also indicates that the chosen spreaders are more uniform, and there is more space between seed nodes. These results validate that the adaptive voting approach in the form of learning has a significantly higher performance compared to the heuristic approaches that are fixed and thus offers an effective influence maximization tool in complex networks.