Random walk-based algorithm for distance-aware influence maximization on multiple query locations

Abstract

The problem with distance-aware influence maximization on multiple query locations (DIM-MQL) is selecting a group of nodes in the network to influence the nodes in the widest range possible near multiple query locations. A random walk-based algorithm for the DIM-MQL problem is presented. To accelerate query processing in real-time, our method involves offline and online processing. Offline processing conducts computations that are independent of the queries, and online processing answers queries in real-time. For offline processing, an algorithm is presented to estimate the upper and lower bounds of the influence spreading of the nodes based on a set of anchor points. We propose an algorithm to sample the influence spreading paths and estimate the influence spreading of the nodes. The number of samples required is analyzed and estimated. Based on the random walk approach, an algorithm is proposed to select anchor points by partitioning the nodes into groups. An algorithm is presented for seed selection in online processing. Based on the spreading bounds obtained in offline processing, a pruning technique is employed to accelerate query processing. Our empirical results show that the proposed algorithm can obtain a larger distance-aware influence spreading than other approaches.