LGIM: A Global Selection Algorithm Based on Local Influence for Influence Maximization in Social Networks

Abstract

Influence maximization is to select k nodes from social networks to maximize the expected number of nodes activated by these selected nodes. Influence maximization problem plays a vital role in commercial marketing, news propagation, rumor control and public services. However, the existing algorithms for influence maximization usually tend to select one aspect from efficiency and accuracy as its main improving objective. This method of excessively pursuing one metric often leads to performing poorly in other metrics. Hence, we think that algorithms for influence maximization should make a suitable compromise between computation efficiency and result accuracy instead of excessively pursuing for one metric. Based on the above understanding, this paper proposes a new algorithm, called Global Selection Based on Local Influence (LGIM). The basic idea of the proposed algorithm is following: if a node can influence another node with large influence, the node also has large influence. Therefore, a two-stage filtering strategy of candidate nodes is proposed, which can reduce a large number of running time. Moreover, this paper also proposes a new objective function to estimate the influence spread of a node set. In summarize, the proposed algorithm utilizes the two-stage filtering strategy of candidate nodes to avoid unnecessary computation, and adopts a new objective function to replace time-consuming Monte-Carle simulations. Experimental results on six real-world social networks demonstrate that the proposed algorithm outperforms other four comparison algorithms when comprehensively considering computation efficiency and result accuracy.

A Discrete Particle Swarm Optimization Algorithm Based on Neighbor Cognition to Solve the Problem of Social Influence Maximization

“…At present, researchers have proposed a series of influence estimation functions, such as EDV proposed by Jiang; a fast approximation method of influence diffusion proposed by Lee and Chuang [14]; Wang designed an effective fitness function based on local influence to Estimation of influence diffusion, that is, the direct neighbors of the seed set are the main factors affecting the spread of integrated circuit models; Qiu [15] proposed LFV to estimate the influence value of a single node; Gong proposed LIE and Qiu proposed EDIV, EDIV combines the LFV function to calculate the two-hop range of local effects, etc. Liu et al [16] proposed a new centrality metric algorithm, which is not only based on the nearest neighbors of a node, but also takes into account the neighbor nodes within two and three hops of a node.…”

A Local Search Algorithm for the Influence Maximization Problem

“…They showed that CELF can achieve up to 700 times faster than the simple greedy algorithm. Whereas, CELF has a poor performance in large network since it has to compute the marginal influence spread of each alternative node repeatedly [21]. [22] designed new schemes to optimize the greedy algorithm under the IC model, by which they generated a faster greedy algorithm based on CELF.…”

“…Recently, [16] proposed a discrete shuffled frogleaping algorithm for the IMP, which selects influential nodes based on network topology characteristic. In [21], Qin et al introduced a discount-degree descending technology and lazyforward technology to identify a set of candidate nodes, based on which they designed a two-stage selection algorithm for the IMP in social networks. [6] proposed a path-based approach, which uses the degree and the independent influence path to estimate the influence spread and uses a heuristic method to reduce the computation volume.…”

Maximizing the influence spread in social networks: a learning automata driven discrete butterfly optimization algorithm

“…In recent years, many researchers are drawn to the problem on how to improve the efficiency of influence maximization algorithms, especially in large-scale social networks. Heuristic algorithms (Wang et al 2016;Liqing et al 2020;Qiu et al 2019) based on network topology were wide spreadly adopted to solve the influence maximization. However, such approaches usually obtain more efficiently solutions than greedy algorithms by sacrificing large accuracy, which is inapplicable to practical scenarios.…”