Matches in ScholarlyData for { ?s ?p ?o. }
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- item-1 hasContent mehdi-kaytoue.
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- 20 creator manisha-pujari.
- 20 creator rushed-kanawati.
- 20 type InProceedings.
- 20 label "Supervised rank aggregation approach for link prediction in complex networks".
- 20 sameAs 20.
- 20 abstract "In this paper we propose a new topological approach for link prediction in dynamic complex networks. The proposed approach applies a supervised rank aggregation method. This functions as follows: first we rank the list of unlinked nodes in a network at instant $t$ according to different topological measures (nodes characteristics aggregation, nodes neighborhood based measures, distance based measures, etc). Each measure provides its own rank. Observing the network at instant $t+1$ where some new links appear, we weight each topological measure according to its performances in predicting these observed new links. These learned weights are then used in a modified version of classical computational social choice algorithms (such as Borda, Kemeny, etc) in order to have a model for predicting new links. We show the effectiveness of this approach through different experimentations applied to co-authorship networks extracted from the DBLP bibliographical database. Results we obtain are also compared with the outcome of classical supervised machine learning based link prediction approaches applied to the same datasets.".
- 20 hasAuthorList authorList.
- 20 isPartOf proceedings.
- 20 keyword "Dynamic social network analysis".
- 20 keyword "Link prediction".
- 20 keyword "Supervised rank aggregation".
- 20 keyword "academic collaboration networks".
- 20 title "Supervised rank aggregation approach for link prediction in complex networks".
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- 21 creator anis-najar.
- 21 creator ludovic-denoyer.
- 21 creator patrick-gallinari.
- 21 type InProceedings.
- 21 label "Predicting Information Diffusion on Social Networks With Partial Knowledge".
- 21 sameAs 21.
- 21 abstract "Models of information diffusion and propagation over large social media usually rely on a \textit{Close World Assumption}: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are non realistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user sets. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.".
- 21 hasAuthorList authorList.
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- 21 keyword "Information Propagation".
- 21 keyword "Machine Learning".
- 21 keyword "Prediction".
- 21 title "Predicting Information Diffusion on Social Networks With Partial Knowledge".
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- item-1 hasContent anis-najar.
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- 25 creator bernardo-huberman.
- 25 creator christina-aperjis.
- 25 creator chunyan-wang.
- 25 creator mao-ye.
- 25 creator thomas-sandholm.
- 25 type InProceedings.
- 25 label "Collective Attention and the Dynamics of Group Deals".
- 25 sameAs 25.
- 25 abstract "We present a study of the group purchasing behavior of daily deals in Groupon and LivingSocial and formulate a predic- tive dynamic model of collective attention for group buying behavior. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time. We find that Groupon deals are easier to predict accurately earlier in the deal lifecycle than LivingSocial deals due to the final number of deal purchases saturating quicker. One possible explana- tion for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial, whereas in Groupon it is based on a collective threshold which is reached very early. Furthermore, the personal benefit of propagating a deal is greater in LivingSocial.".
- 25 hasAuthorList authorList.
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- 25 keyword "collective attention".
- 25 keyword "group deals".
- 25 keyword "purchase dynamics".
- 25 title "Collective Attention and the Dynamics of Group Deals".
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