Conclusion

Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

There are many open problems in the area of diffusion in social networks. First, we believe that data-driven approaches, such as those described in the Chap. 7, are really still in the early stages of development. We have noted that recent work of this type deals with issues such as predicting the influence of individuals nodes, predicting the outcome of a diffusion process, and identifying more realistic models. Work in this area spans from observational studies in disciplines such as sociology and economics to the machine learning approaches seen in the computer science community. As data on real-world diffusion traces become more available, we expect this line of work to grow further.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Number of pages1
Edition9783319231044
DOIs
StatePublished - Jan 1 2015

Publication series

NameSpringerBriefs in Computer Science
Number9783319231044
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

Keywords

  • Diffusion process
  • Open problem
  • Practical issue
  • Realistic model
  • Social network

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., & Guo, R. (2015). Conclusion. In SpringerBriefs in Computer Science (9783319231044 ed.). (SpringerBriefs in Computer Science; No. 9783319231044). Springer. https://doi.org/10.1007/978-3-319-23105-1_8