Supervised rank aggregation approach for link prediction in complex networks

Pujari, Manisha; Kanawati, Rushed

doi:10.1145/2187980.2188260

Cited by 42 publications

(24 citation statements)

References 17 publications

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“…To overcome this disadvantage a number of supervised methods have recently been proposed. Several of these methods have explored weighted aggregation rules [36,29], where a well explored social choice aggregation rule, such as Borda or Kemeny, are applied to weighted expert preferences. The weights are tuned on the training data to reflect each experts "agreement" with the ground truth preferences.…”

Section: Score-based Methodsmentioning

confidence: 99%

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CRF framework for supervised preference aggregation

Volkovs

Zemel

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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We develop a flexible Conditional Random Field framework for supervised preference aggregation, which combines preferences from multiple experts over items to form a distribution over rankings. The distribution is based on an energy comprised of unary and pairwise potentials allowing us to effectively capture correlations between both items and experts. We describe procedures for learning in this model and demonstrate that inference can be done much more efficiently than in analogous models. Experiments on benchmark tasks demonstrate significant performance gains over existing rank aggregation methods.

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Section: Score-based Methodsmentioning

confidence: 99%

“…The supervised problem has received considerable attention in recent years and a number of supervised approaches have been proposed [20,36,37,29]. Notably, [37] has recently shown that by applying SVD factorization to pairwise preference matrices effective item features can be extracted.…”

Section: Introductionmentioning

confidence: 99%

CRF framework for supervised preference aggregation

Volkovs

Zemel

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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“…According to these weights is reduced the number of features taken in input by the regression algorithm used for prediction. A rank aggregation approach is proposed in Pujari and Kanawati (2012). The authors rank the list of unlinked nodes according to some topological measures, then at the new instant time each measure is weighted according to its performance in predicting new links.…”

Section: Related Workmentioning

confidence: 99%

A supervised approach for intra-/inter-community interaction prediction in dynamic social networks

Rossetti

Guidotti

Miliou

et al. 2016

Soc. Netw. Anal. Min.

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Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intracommunity and inter-community link prediction.Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.

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“…Pujari et al [36] presented a supervised rank aggregation method for link prediction in complex networks. Vu et al [40] introduced a continuous-time regression model for network link prediction.…”

Section: Related Workmentioning

confidence: 99%

A fast algorithm for predicting links to nodes of interest

Chen

2016

Information Sciences

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The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can.

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Supervised rank aggregation approach for link prediction in complex networks

Cited by 42 publications

References 17 publications

CRF framework for supervised preference aggregation

CRF framework for supervised preference aggregation

A supervised approach for intra-/inter-community interaction prediction in dynamic social networks

A fast algorithm for predicting links to nodes of interest

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