The main purpose of influence maximization is to find a subset of key nodes that could maximize the spread of information under a certain diffusion model. In recent years, many studies have focused on the problem of influence maximization. However, these studies usually ignore the role of community structure which captures a significant effect on the process of influence propagation. To address above problem, we propose a novel hybrid algorithm PHG, which is a three-phase algorithm for influence maximization based on community structure. In our algorithm the influence propagation process is divided into three phases: 1) partition phase; 2) heuristic phase; and 3) greedy phase. Specifically, we first design an efficient algorithm CCSC that finds key nodes in each community to construct a candidate set by detecting community structure. Second, we find the most potential influence nodes from a candidate set by combing the influence weight of nodes and the community influence of nodes through the analysis of the community structure of the impact on nodes. Finally, we greedily select the nodes with maximization marginal gain from remaining a candidate set. The extensive experimental results on artificial and real-world social networks show that our algorithm obtains a better influence spread as well as an acceptable running time.
The problem of influence maximization aims to specify the small number of initial individuals that will eventually influence the individuals as much as possible, which has aroused wide attention of researchers. However, the most existing work is limited to the static social network and ignores the role of time in information propagation. In this paper, we analyze the influence maximization problem in temporal social networks and present a greedy-based on the latency-aware independent cascade (GLAIC) algorithm enhanced by cost-effective lazy forward optimization based on the latency-aware independent cascade model to capture the dynamic aspect of real-world social networks. Moreover, we modify the distribution of influence delays in the LAIC model by considering power-law distribution. At last, we carry out extensive experiments over the real-world networks, which demonstrate that our proposal achieves an excellent performance to other related algorithms.
The purpose of influence maximization problem is to select a small seed set to maximize the number of nodes influenced by the seed set. For viral marketing, the problem of influence maximization plays a vital role. Current works mainly focus on the unsigned social networks, which include only positive relationship between users. However, the influence maximization in the signed social networks including positive and negative relationships between users is still a challenging issue. Moreover, the existing works pay more attention to the positive influence. Therefore, this paper first analyzes the positive maximization influence in the signed social networks. The purpose of this problem is to select the seed set with the most positive influence in the signed social networks. Afterwards, this paper proposes a model that incorporates the state of node, the preference of individual and polarity relationship, called Independent Cascade with the Negative and Polarity (ICWNP) propagation model. On the basis of the ICWNP model, this paper proposes a Greedy with ICWNP algorithm. Finally, on four real social networks, experimental results manifest that the proposed algorithm has higher accuracy and efficiency than the related methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.