Abstract
One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model.
Highlights
One company develops some new products and wants to market them
K-shell decomposition[15] and Pagerank[16] are a good method to measure the influence spread capability of nodes. They could fast select top-k nodes based on their intuition attributes such as Degree: a simple method that selects the k nodes with the highest degree; Distance: a simple method that selects the k vertices with the smallest average shortest-path distances to all other nodes, which is evaluated in literature[6]
Because different algorithms are applied to different propagation models, the accuracy and running time can not be compared to all proposed algorithms
Summary
One company develops some new products and wants to market them. It has a limited budget so that it can only select a small group of initial users to experience the products. They propose a propagation-based algorithm (called general greedy algorithm) It performs well in accuracy, which wholly depends on computer operation on each round of influence estimation and it is a continuously greedy optimizing process. For overcoming the drawback of propagation-based algorithm, Leskovec et al.[4] present an optimization in selecting seeds, which is referred to as the Cost-Effective LazyForward (CELF) It uses the sub-modularity[8] property of the influence maximization objective to greatly reduce the number of evaluations on the influence spread of nodes. Goyal et al.[9] proposed CELF++algorithm to optimize the running time, but still has this problem in large-scale networks. These algorithms above are all belong to topology-based algorithm
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