Actively learning to infer social ties

Zhuang, Honglei; Tang, Jie; Tang, William; Lou, Tiancheng; Chin, Alvin; Wang, Xia

doi:10.1007/s10618-012-0274-x

Cited by 27 publications

(20 citation statements)

References 33 publications

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“…However, these methods cannot be parallelized and are applicable to only small datasets. 4 Algorithm 1 Greedy Algorithm for Constructing a k-Order t-Cherry Junction Tree It can be shown that the overall time to construct a junction tree is dominated by the table construction phase on average, and therefore decreasing the time needed to construct the table will greatly decrease the time needed to build a t-cherry junction tree. To get a more concrete sense, we run the greedy algorithm using a Twitter data set [16], using 15 binary random variables and vary the width of the tree generated from 2 to 14.…”

Section: Data-driven Construction Of a T-cherry Junction Treementioning

confidence: 99%

“…A t-cherry junction tree is a structure that has theoretical guarantees on accuracy of approximation, as well as allowing for efficient, exact inference once it is constructed. Other graphical models are often used, such as factor graphs [4], but these offer no guarantees on their approximation and may not convergence when performing inference [5]. Notably, when using a junction tree, any dependence loops among random variables, which often occur in social relationships, can be easily handled by incorporating those This research was supported in part by the U.S. National Science Foundation under Grant CNS-1218484, and DoD MURI project No.…”

Section: Introductionmentioning

confidence: 99%

“…We do not incorporate attribute data of the users, as other works have [4] [7]. Once a new user begins to make connections to other users in the network, the probability that this user will connect with different users in the network must be updated so that the most likely social connections are recommended.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling social network relationships via t-cherry junction trees

Proulx

Zhang

2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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The massive scale of online social networks makes it very challenging to characterize the underlying structure therein. In this paper, we employ the t-cherry junction tree, a very recent advancement in probabilistic graphical models, to develop a compact representation and good approximation of an otherwise intractable model for users' relationships in a social network. There are a number of advantages in this approach: 1) the best approximation possible via junction trees belongs to the class of t-cherry junction trees; 2) constructing a tcherry junction tree can be largely parallelized; and 3) inference can be performed using distributed computation. To improve the quality of approximation, we also devise an algorithm to build a higher order tree gracefully from an existing one, without constructing it from scratch. We apply this approach to Twitter data containing 100,000 nodes and study the problem of recommending connections to new users.

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Section: Data-driven Construction Of a T-cherry Junction Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling social network relationships via t-cherry junction trees

Proulx

Zhang

2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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“…The database systems should not only model the trajectories but also represent the social relationships of moving objects. More and more, real-world applications require ad hoc data management technologies, leading to a rise in new study challenges and opportunities in the field of MOD and geographical information systems (GIS) [21,[25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Querying Moving Objects with Social Relationships

Zhang

Lü

2016

IJGI

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Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying.

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“…The paper "Actively Learning to Infer Social Ties" (Zhuang et al 2012), investigates how to predict social relationships between people in a large online social network. This problem is of practical importance for many emerging applications, but at the same time it poses several challenges, such as how to exploit the few labelled relationships, or how to make optimal use of user interaction.…”

mentioning

confidence: 99%