DynaDiffuse

A dynamic diffusion model for continuous time constrained influence maximization

Miao Xie, Qiusong Yang, Qing Wang, Gao Cong, Gerard De Melo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DynaDiffuse. Although the problem is NP-hard, the influence spread functions are monotonie and submodular, enabling fast approximations on top of an innovative stochastic model checking approach. Experiments on real social network data show that our model finds higher quality solutions and our algorithm outperforms state-of-art alternatives.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PublisherAI Access Foundation
Pages346-352
Number of pages7
ISBN (Electronic)9781577356998
StatePublished - Jun 1 2015
Externally publishedYes
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: Jan 25 2015Jan 30 2015

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
CountryUnited States
CityAustin
Period1/25/151/30/15

Fingerprint

Model checking
Stochastic models
Computational complexity
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Xie, M., Yang, Q., Wang, Q., Cong, G., & De Melo, G. (2015). DynaDiffuse: A dynamic diffusion model for continuous time constrained influence maximization. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 (pp. 346-352). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1). AI Access Foundation.
Xie, Miao ; Yang, Qiusong ; Wang, Qing ; Cong, Gao ; De Melo, Gerard. / DynaDiffuse : A dynamic diffusion model for continuous time constrained influence maximization. Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015. AI Access Foundation, 2015. pp. 346-352 (Proceedings of the National Conference on Artificial Intelligence).
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title = "DynaDiffuse: A dynamic diffusion model for continuous time constrained influence maximization",
abstract = "Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DynaDiffuse. Although the problem is NP-hard, the influence spread functions are monotonie and submodular, enabling fast approximations on top of an innovative stochastic model checking approach. Experiments on real social network data show that our model finds higher quality solutions and our algorithm outperforms state-of-art alternatives.",
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year = "2015",
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language = "English (US)",
series = "Proceedings of the National Conference on Artificial Intelligence",
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Xie, M, Yang, Q, Wang, Q, Cong, G & De Melo, G 2015, DynaDiffuse: A dynamic diffusion model for continuous time constrained influence maximization. in Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015. Proceedings of the National Conference on Artificial Intelligence, vol. 1, AI Access Foundation, pp. 346-352, 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015, Austin, United States, 1/25/15.

DynaDiffuse : A dynamic diffusion model for continuous time constrained influence maximization. / Xie, Miao; Yang, Qiusong; Wang, Qing; Cong, Gao; De Melo, Gerard.

Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015. AI Access Foundation, 2015. p. 346-352 (Proceedings of the National Conference on Artificial Intelligence; Vol. 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - DynaDiffuse

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AU - Xie, Miao

AU - Yang, Qiusong

AU - Wang, Qing

AU - Cong, Gao

AU - De Melo, Gerard

PY - 2015/6/1

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N2 - Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DynaDiffuse. Although the problem is NP-hard, the influence spread functions are monotonie and submodular, enabling fast approximations on top of an innovative stochastic model checking approach. Experiments on real social network data show that our model finds higher quality solutions and our algorithm outperforms state-of-art alternatives.

AB - Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DynaDiffuse. Although the problem is NP-hard, the influence spread functions are monotonie and submodular, enabling fast approximations on top of an innovative stochastic model checking approach. Experiments on real social network data show that our model finds higher quality solutions and our algorithm outperforms state-of-art alternatives.

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M3 - Conference contribution

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BT - Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015

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Xie M, Yang Q, Wang Q, Cong G, De Melo G. DynaDiffuse: A dynamic diffusion model for continuous time constrained influence maximization. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015. AI Access Foundation. 2015. p. 346-352. (Proceedings of the National Conference on Artificial Intelligence).