Identifying opinion leader nodes in online social networks with a new closeness evaluation algorithm
In online social networks, there are some influential opinion leader nodes who can be used to accelerate the spread of positive information and suppress the diffusion of rumors. If these opinion leaders can be identified timely and correctly, there will be contributing to guide the popular opinions. The closeness is introduced for mapping the relationship between the nodes according to the different interaction types in online social network. In order to measure the impact of the information transmission between non-adjacent nodes in online social networks, a closeness evaluating algorithm of the adjacent nodes and the non-adjacent nodes is given based on the relational features between users. By using the algorithm, the closeness between the adjacent nodes and the non-adjacent nodes can obtained depending on the interaction time of nodes and the delay of their hops. Furthermore, a more accurate and efficient betweenness centrality scheme based on the optimized algorithm with the degree of closeness and the corresponding updating strategy. The opinion leader nodes should be identified more accurately and efficiently under the improved algorithm because the considering of closeness between nodes in the network. Finally, the maximum spreading experiment is done for comparing the proposed method with other existing identifying opinion leader selecting schemes based on the Independent Cascade Model. The result of experiment shows the effectiveness and practicality of the evaluating algorithm.
KeywordsSocial networks Closeness Independent cascade Opinion leader nodes
We would like to thank the anonymous reviewers for their careful reading and useful comments. This work was supported by the National Natural Science Foundation of China (U1405255, 61202390), the China 111 Project (B16037), the Foundation of Science and Technology on Information Assurance Laboratory (KJ-14-109) and the Fundamental Research Funds for the Central Universities (JB161505).
Compliance with ethical standards
Conflict of interest
Li Yang, Yafeng Qiao, Zhihong Liu, Jianfeng Ma and Xinghua Li declare that there are no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bader DA, Kintali S, Madduri K, Mihail M (2007) Approximating betweenness centrality. In: Bonato A, Chung FRK (eds) Algorithms and models for the web-graph. Springer, San Diego, pp 124–137Google Scholar
- Bakshy E, Karrer B, Adamic LA (2009) Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM conference on electronic commerce, ACM, pp 325–334Google Scholar
- Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 57–66Google Scholar
- Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41Google Scholar
- Fu Z, Ren K, Shu J, Sun X (2016) Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans Parallel Distrib Syst 27(9):2546–2559Google Scholar
- Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380Google Scholar
- Guo P, Wang J, Geng XH, Kim CS, Kim JU (2014) A variable threshold-value authentication architecture for wireless mesh networks. J Internet Technol 15(6):929–935Google Scholar
- Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 137–146Google Scholar
- Li J, Li J, Chen X, Jia C, Lou W (2015) Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans Comput 64(2):425–437Google Scholar
- Putzke J, Takeda H (2016) Identifying key opinion leaders in evolving co-authorship networksa descriptive study of a proxy variable for betweenness centrality. In: Cherifi H, Gonçalves B, Menezes R, Sinatra R (eds) Complex networks, vol VII. Springer, Switzerland, pp 311–323Google Scholar
- Qin Y, Ma J, Gao S (2016) Efficient influence maximization under TSCM: a suitable diffusion model in online social networks. Soft Comput 1–12. doi: 10.1007/s00500-016-2068-3
- Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 61–70Google Scholar
- Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: Lovrek I, Howlett RJ, Jain LC (eds) Knowledge-based intelligent information and engineering systems. Springer, Croatia, pp 67–75Google Scholar
- Segarra S, Ribeiro A (2014) A stable betweenness centrality measure in networks. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 3859–3863Google Scholar
- Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178Google Scholar
- Wang M, Ma J (2015) A novel recommendation approach based on users weighted trust relations and the rating similarities. Soft Comput 1–10. doi: 10.1007/s00500-015-1734-1
- Xia Z, Wang X, Sun X, Wang Q (2015) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst 27(2):1–1Google Scholar