Skip to main content

How to Choose Friends Strategically

  • Conference paper
  • First Online:
Structural Information and Communication Complexity (SIROCCO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10641))

Abstract

Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person v in the network has a certain threshold t(v) for activation, i.e. adoption of the product or idea. If v has at least t(v) activated neighbors, then v will also become activated. If Alice wants to make k new friends in the network, and thereby activate the most number of people, how should she choose these friends? We study the problem of choosing the k people in the network to befriend, who will in turn activate the maximum number of people. This Maximum Influence with Links Problem has applications in viral marketing and the study of epidemics. We show that the solution can be quite different from the related and widely studied influence maximization problem where the objective is to choose a seed or target set with maximum influence. We prove that the Maximum Influence with Links problem is NP-complete even for bipartite graphs in which all nodes have threshold 1 or 2. In contrast, we give polynomial time algorithms that find optimal solutions for the problem for trees, paths, cycles, and cliques.

Research supported by NSERC, Canada.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ben-Zwi, O., Hermelin, D., Lokshtanov, D., Newman, I.: Treewidth governs the complexity of target set selection. Discrete Optim. 8, 702–715 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  2. Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, SODA 2014, pp. 946–957 (2014)

    Google Scholar 

  3. Brown, J.J., Reingen, P.H.: Social ties and word-of-mouth referral behavior. J. Consum. Res. 14, 350–362 (1987)

    Article  Google Scholar 

  4. Chen, N.: On the approximability of influence in social networks. In: Proceedings of the Symposium on Discrete Algorithms, SODA 2008, pp. 1029–1037 (2008)

    Google Scholar 

  5. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 199–208 (2009)

    Google Scholar 

  6. Cicalese, F., Cordasco, G., Gargano, L., Milanic, M., Peters, J.G., Vaccaro, U.: How to go viral: cheaply and quickly. In: Ferro, A., Luccio, F., Widmayer, P. (eds.) Fun with Algorithms. LNCS, vol. 8496, pp. 100–112. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07890-8_9

  7. Cicalese, F., Cordasco, G., Gargano, L., Milanič, M., Vaccaro, U.: Latency-bounded target set selection in social networks. In: Bonizzoni, P., Brattka, V., Löwe, B. (eds.) CiE 2013. LNCS, vol. 7921, pp. 65–77. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39053-1_8

    Chapter  Google Scholar 

  8. Cordasco, G., Gargano, L., Rescigno, A.A., Vaccaro, U.: Optimizing spread of influence in social networks via partial incentives. In: Scheideler, C. (ed.) Structural Information and Communication Complexity. LNCS, vol. 9439, pp. 119–134. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25258-2_9

    Chapter  Google Scholar 

  9. de Caen, D.: An upper bound on the sum of squares of degrees in a graph. Discrete Math. 185, 245–248 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  10. Demaine, E.D., Hajiaghayi, M.T., Mahini, H., Malec, D.L., Raghavan, S., Sawant, A., Zadimoghadam, M.: How to influence people with partial incentives. In: Proceedings of the International Conference on World Wide Web, WWW 2014, pp. 937–948 (2014)

    Google Scholar 

  11. Dinh, T.N., Zhang, H., Nguyen, D.T., Thai, M.T.: Cost-effective viral marketing for time-critical campaigns in large-scale social networks. IEEE/ACM Trans. Netw. 22, 2001–2011 (2014)

    Article  Google Scholar 

  12. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 57–66 (2001)

    Google Scholar 

  13. Eftekhar, M., Ganjali, Y., Koudas, N.: Information cascade at group scale. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 401–409 (2013)

    Google Scholar 

  14. Fazli, M.A., Ghodsi, M., Habibi, J., Jalaly Khalilabadi, P., Mirrokni, V., Sadeghabad, S.S.: On the non-progressive spread of influence through social networks. In: Fernández-Baca, D. (ed.) LATIN 2012. LNCS, vol. 7256, pp. 315–326. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29344-3_27

    Chapter  Google Scholar 

  15. Gargano, L., Hell, P., Peters, J., Vaccaro, U.: Influence diffusion in social networks under time window constraints. In: Moscibroda, T., Rescigno, A.A. (eds.) SIROCCO 2013. LNCS, vol. 8179, pp. 141–152. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03578-9_12

    Chapter  Google Scholar 

  16. Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12, 211–223 (2001)

    Article  Google Scholar 

  17. Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. Proc. VLDB Endow. 5, 73–84 (2011)

    Article  Google Scholar 

  18. Goyal, A., Bonchi, F., Lakshmanan, L.V.S., Venkatasubramanian, S.: On minimizing budget and time in influence propagation over social networks. Soc. Netw. Anal. Min. 3, 179–192 (2013)

    Article  Google Scholar 

  19. Goyal, A., Lu, W., Lakshmanan, L.V.S.: Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the International Conference Companion on World Wide Web, WWW 2011, pp. 47–48 (2011)

    Google Scholar 

  20. Gunnec, D., Raghavan, S.: Integrating social network effects in the share-of-choice problem. Technical report, University of Maryland, College Park (2012)

    Google Scholar 

  21. Gunnec, D., Raghavan, S., Zhang, R.: The least cost influence problem. Technical report, University of Maryland, College Park (2013)

    Google Scholar 

  22. He, J., Ji, S., Beyah, R., Cai, Z.: Minimum-sized influential node set selection for social networks under the independent cascade model. In: Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2014, pp. 93–102 (2014)

    Google Scholar 

  23. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146 (2003)

    Google Scholar 

  24. Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005). https://doi.org/10.1007/11523468_91

    Chapter  Google Scholar 

  25. Lafond, M., Narayanan, L., Wu, K.: Whom to befriend to influence people. In: Suomela, J. (ed.) SIROCCO 2016. LNCS, vol. 9988, pp. 340–357. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48314-6_22

    Chapter  Google Scholar 

  26. Lamba, H., Pfeffer, J.: Maximizing the spread of positive influence by deadline. In: Proceedings of the International Conference Companion on World Wide Web, WWW 2016, pp. 67–68 (2016)

    Google Scholar 

  27. Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. In: Proceedings of the ACM Conference on Electronic Commerce, pp. 228–237 (2006)

    Google Scholar 

  28. Lu, W., Bonchi, F., Goyal, A., Lakshmanan, L.V.S.: The bang for the buck: fair competitive viral marketing from the host perspective. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 928–936 (2013)

    Google Scholar 

  29. Lv, S., Pan, L.: Influence maximization in independent cascade model with limited propagation distance. In: Han, W., Huang, Z., Hu, C., Zhang, H., Guo, L. (eds.) APWeb 2014. LNCS, vol. 8710, pp. 23–34. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11119-3_3

    Google Scholar 

  30. Mossel, E., Roch, S.: Submodularity of influence in social networks: from local to global. SIAM J. Comput. 39, 2176–2188 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  31. Nguyen, H.T., Thai, M.T., Dinh, T.N.: Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. In: Proceedings of the International Conference on Management of Data, SIGMOD 2016, pp. 695–710 (2016)

    Google Scholar 

  32. Nichterlein, A., Niedermeier, R., Uhlmann, J., Weller, M.: On tractable cases of target set selection. Soc. Netw. Anal. Min. 3(2), 233–256 (2012)

    Article  MATH  Google Scholar 

  33. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 61–70 (2002)

    Google Scholar 

  34. Tang, S., Yuan, J.: Going viral: optimizing discount allocation in social networks for influence maximization (2016). CoRR abs/1606.07916

  35. Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, pp. 1539–1554 (2015)

    Google Scholar 

  36. Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pp. 75–86 (2014)

    Google Scholar 

  37. Yang, Y., Mao, X., Pei, J., He, X.: Continuous influence maximization: what discounts should we offer to social network users? In: Proceedings of the International Conference on Management of Data, SIGMOD 2016, pp. 727–741 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lata Narayanan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Narayanan, L., Wu, K. (2017). How to Choose Friends Strategically. In: Das, S., Tixeuil, S. (eds) Structural Information and Communication Complexity. SIROCCO 2017. Lecture Notes in Computer Science(), vol 10641. Springer, Cham. https://doi.org/10.1007/978-3-319-72050-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72050-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72049-4

  • Online ISBN: 978-3-319-72050-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics