Pranay Lohia
Srikanta Bedathur
ABSTRACT
Using social networks for spreading marketing information is a commonly used strategy to help in quick adoption of innovations, retention of customers and for improving brand awareness. In many settings, the set of entities in the network who must be the targets of such an information spread are already known, either implicitly or explicitly. It would still be beneficial to route the information to them through a carefully chosen set of influencers in the network. We term networks where we have such vertices labeled as targeted recipients as targeted networks. For instance, in an online marketing channel of a fashion product, where vertices are tagged with 'fashion' as their preferred choice of online shopping, forms a targeted network. In such targeted networks, how to select a small subset of vertices that maximizes the influence over target nodes while simultaneously minimizing the non-target nodes which get the information (e.g., to reduce their spam, or in some cases, due to costs)? We term this as the problem of maximizing the marginal influence over target networks and propose an iterative algorithm to solve this problem. We present the results of our experiment with large information networks, derived from English Wikipedia graph, which show that the proposed algorithm effectively identifies influential nodes that help reach pages identified through queries/topics. Qualitative analysis of our results shows that we can generate a semantically meaningful ranking of query-specific influential nodes.
- Apache. [n. d.]. Lucene Indexer. https://lucene.apache.org/core/.Google Scholar
- Marcel Blattner, Yi-Cheng Zhang, and Sergei Maslov. 2007. Exploring an opinion network for taste prediction: An empirical study. Physica A: Statistical Mechanics and its Applications 373 (2007), 753--758.Google Scholar
- Ralf Caers and Vanessa Castelyns. 2010. LinkedIn and Facebook in Belgium: The influences and biases of social network sites in recruitment and selection procedures. Social Science Computer Review (2010), 0894439310386567.Google Scholar
- Wei Chen, Yajun Wang, and Siyu Yang. 2009. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 199--208.Google ScholarDigital Library
- IBM. [n. d.]. SaND (Social Networks and Discovery). https://www-10.lotus.com/ldd/lcwiki.nsf/xpAPIViewer.xsp?lookupName=IBM+Connections+5.0+API+Documentation&action=openDocument&res_title=Social_Network_(Graph)_API_ic50&content=apicontent.Google Scholar
- Zhaoyan Jin, Quanyuan Wu, Dianxi Shi, and Huining Yan. 2013. Random Walk Based Inverse Influence Research in Online Social Networks. In High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), 2013 IEEE 10th International Conference on. IEEE, 2206--2213.Google ScholarCross Ref
- David Kempe, Jon Kleinberg, and Éva Tardos. 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, 137--146.Google ScholarDigital Library
- Fa-Hsien Li, Cheng-Te Li, and Man-Kwan Shan. 2011. Labeled influence maximization in social networks for target marketing. In Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). IEEE, 560--563.Google ScholarCross Ref
- Yanhua Li, Wei Chen, Yajun Wang, and Zhi-Li Zhang. 2013. Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 657--666.Google ScholarDigital Library
- Kok Lim, Hur Ewing-Chow, Teddy Jasin, Zaidah Mohd, Torres Oey, Wee Ong, Heng Shi, Julian Vincent, Kian Vu, Heling Zhang, et al. 2005. Social marketing network. US Patent App. 11/233, 855.Google Scholar
- Silviu Maniu and Bogdan Cautis. 2013. Network-aware search in social tagging applications: Instance optimality versus efficiency. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 939--948.Google ScholarDigital Library
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: Bringing order to the web. (1999).Google Scholar
- Rama Kumar Pasumarthi, Ramasuri Narayanam, and Balaraman Ravindran. 2014. Targeted Influence Maximization through a Social Network. (2014).Google Scholar
- Ralf Schenkel, Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane X Parreira, and Gerhard Weikum. 2008. Efficient top-k querying over social-tagging networks. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 523--530.Google ScholarDigital Library
- Antti Ukkonen, Carlos Castillo, Debora Donato, and Aristides Gionis. 2008. Searching the wikipedia with contextual information. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 1351--1352.Google ScholarDigital Library
- Peifeng Yin, Wang-Chien Lee, and Ken CK Lee. 2010. On top-k social web search. In Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, 1313--1316.Google ScholarDigital Library
- Justin Zhan, Sweta Gurung, and Sai Phani Krishna Parsa. 2017. dentification of top-K nodes in large networks using Katz centrality. Journal of Big Data 4, 1, 16. https://doi.org/10.1186/s40537-017-0076-5Google ScholarCross Ref
- Huiyuan Zhang, Thang N Dinh, and My T Thai. 2013. Maximizing the Spread of Positive Influence in Online Social Networks. In Distributed Computing Systems (ICDCS), 2013 IEEE 33rd International Conference on. IEEE, 317--326.Google ScholarDigital Library
Recommendations
Discovering influencers for marketing in the blogosphere
Discovering influential bloggers will not only allow us to understand better the social activities taking place in the blogosphere, but will also provide unique opportunities for sales and advertising. In this paper, we develop an MIV (marketing ...
How do influencers mention brands in social media?: sponsorship prediction of Instagram posts
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningBrand mentioning is a type of word-of-mouth advertising method where a brand name is disclosed by social media users in posts. Recently, brand mentioning by influencers has raised great attention because of the strong viral effects on the huge fan base ...
Brand communication through digital influencers: Leveraging blogger engagement
The growing power of bloggers to influence their connected network has emerged as a new communication venue for brands. This study elaborates upon the role of bloggers in brand communication, and reveals how brands can engage with bloggers, currently ...
Comments