Efficient inference algorithms for network activities

Tran, Long Quoc

Title:

Efficient inference algorithms for network activities

dc.contributor.advisor	Chau, Duen Horng
dc.contributor.author	Tran, Long Quoc
dc.contributor.committeeMember	Zha, Hongyuan
dc.contributor.committeeMember	Song, Le
dc.contributor.committeeMember	Sun, Jimeng
dc.contributor.committeeMember	Zhou, Haomin
dc.contributor.committeeMember	Gray, Alexander G.
dc.contributor.department	Computational Science and Engineering
dc.date.accessioned	2015-06-08T18:20:38Z
dc.date.available	2015-06-08T18:20:38Z
dc.date.created	2015-05
dc.date.issued	2015-01-08
dc.date.submitted	May 2015
dc.date.updated	2015-06-08T18:20:38Z
dc.description.abstract	The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals' activities in the social network. The inference of relationship between users/nodes or groups of users/nodes could be further complicated when activities are interval-censored, that is, when one only observed the number of activities that occurred in certain time windows. The same phenomenon happens in the online advertisement world where the advertisers often offer a set of advertisement impressions and observe a set of conversions (i.e. product/service adoption). In this case, the advertisers desire to know which advertisements best appeal to the customers and most importantly, their rate of conversions. Inspired by these challenges, we investigated inference algorithms that efficiently recover user relationships in both cases: time-stamped data and interval-censored data. In case of time-stamped data, we proposed a novel algorithm called NetCodec, which relies on a Hawkes process that models the intertwine relationship between group participation and between-user influence. Using Bayesian variational principle and optimization techniques, NetCodec could infer both group participation and user influence simultaneously with iteration complexity being O((N+I)G), where N is the number of events, I is the number of users, and G is the number of groups. In case of interval-censored data, we proposed a Monte-Carlo EM inference algorithm where we iteratively impute the time-stamped events using a Poisson process that has intensity function approximates the underlying intensity function. We show that that proposed simulated approach delivers better inference performance than baseline methods. In the advertisement problem, we propose a Click-to-Conversion delay model that uses Hawkes processes to model the advertisement impressions and thinned Poisson processes to model the Click-to-Conversion mechanism. We then derive an efficient Maximum Likelihood Estimator which utilizes the Minorization-Maximization framework. We verify the model against real life online advertisement logs in comparison with recent conversion rate estimation methods. To facilitate reproducible research, we also developed an open-source software package that focuses on various Hawkes processes proposed in the above mentioned works and prior works. We provided efficient parallel (multi-core) implementations of the inference algorithms using the Bayesian variational inference framework. To further speed up these inference algorithms, we also explored distributed optimization techniques for convex optimization under the distributed data situation. We formulate this problem as a consensus-constrained optimization problem and solve it with the alternating direction method for multipliers (ADMM). It turns out that using bipartite graph as communication topology exhibits the fastest convergence.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/53499
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Hawkes
dc.subject	Inference
dc.title	Efficient inference algorithms for network activities
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Chau, Duen Horng
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Computational Science and Engineering
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relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication	01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
thesis.degree.level	Doctoral