DDSE: A novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks

Cui, Laizhong; Hu, Huaixiong; Yu, Shui; Qiao, Yan; Ming, Zhong; Wen, Zhenkun; Lu, Nan

doi:10.1016/j.jnca.2017.12.003

Cited by 110 publications

(53 citation statements)

References 42 publications

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“…It builds a graph network using users as nodes and retweets as edges between users. Researchers have also suggested a variety of algorithms such as InfluenceRank, LeaderRank, TwitterRank, ProfileRank, and Degree Descending Search Evolution (DDSE) algorithm to identify influential users and influence maximization 77,86 …”

Section: Fundamentals Of Snamentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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Section: Fundamentals Of Snamentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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“…Influence maximization was initially proposed by Kempe et al [1] and it aims to select a set of users in a social network to maximize the expected number of influenced users through several information propagation steps [14] . Empirical studies have been performed on influence learning [10,15] , algorithm optimization [16][17][18] , scalability promotion [19][20][21] , and influence of group conformity [4,22] . Saito et al [23] predicted the information diffusion probabilities in social networks under the independent cascade model.…”

Section: Social Influence Analysismentioning

confidence: 99%

Spreading social influence with both positive and negative opinions in online networks

Han

et al. 2019

Big Data Min. Anal.

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Social networks are important media for spreading information, ideas, and influence among individuals. Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the "word-of-mouth" effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set (MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of Â. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.

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“…MI wants to maximize the number of nodes affected by this seed set (called influence spread) [14]. MI algorithm is a key problem in social influence analysis [15]. MI plays an important role in business policy and information spread [10].…”

Section: Maximum Influencementioning

confidence: 99%

“…MI plays an important role in business policy and information spread [10]. There are a large number of references about the MI issue [6,14,15]. The classic MI model uses independent cascade and linear threshold techniques [16,10].…”

Section: Maximum Influencementioning

confidence: 99%

Maximum Recommendation in Geo-social Network for Business

2019

Teh. vjesn.

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Most of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease the recommendation effect but also increase the recommendation cost in the business. To remove the shortcomings, we propose a Maximum Recommendation scheme in Geo-social network for business (called as MRG). On the one hand, we identify k nodes with maximum recommendation according to the expected paid node number k. On the other hand, we exclude the negative node from the geo-social network. Based on the above innovation, we effectively increase the recommendation effect and decrease the company's recommendation cost. Meanwhile, MRG considers the negative influence to enhance the recommendation efficiency. Experimental results show that our scheme has better performance than most of the existing methods in the maximum recommendation field.

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DDSE: A novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks

Cited by 110 publications

References 42 publications

Semantic analysis on social networks: A survey

Semantic analysis on social networks: A survey

Spreading social influence with both positive and negative opinions in online networks

Maximum Recommendation in Geo-social Network for Business

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