Molecular model of dynamic social network based on e-mail communication

Budka, Marcin; Juszczyszyn, Krzysztof; Musiał, Katarzyna; Musiał, Anna

doi:10.1007/s13278-013-0101-4

Cited by 16 publications

(13 citation statements)

References 36 publications

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“…However, it fails to capture meaningful features for the social network examples. This echoes the comment made in Budka et al (2013) that the shortest path distance is not suitable to measure the distances in social networks. We also like to point out that for all tested problems, EDME captured nearly 100% variance and it treats the local features equally important in terms of the leading eigenvalues being of the same magnitude.…”

Section: Numerical Performancesupporting

confidence: 67%

“…(i) The shortest path distance or the distance by the k-nearest neighbor or the unit-ball rule is often not suitable in deriving distances in social network. This point has been emphasized in the recent study on E-mail social network by Budka et al (2013). (ii) MVU and MVE models only depend on the initial distances and do not depend on any particular ways in obtaining them.…”

Section: Embedding Methods In Manifold Learningmentioning

confidence: 88%

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Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction

Ding

2016

Math. Program.

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Classical multidimensional scaling only works well when the noisy distances observed in a high dimensional space can be faithfully represented by Euclidean distances in a low dimensional space. Advanced models such as Maximum Variance Unfolding (MVU) and Minimum Volume Embedding (MVE) use Semi-Definite Programming (SDP) to reconstruct such faithful representations. While those SDP models are capable of producing high quality configuration numerically, they suffer two major drawbacks. One is that there exist no theoretically guaranteed bounds on the quality of the configuration. The other is that they are slow in computation when the data points are beyond moderate size. In this paper, we propose a convex optimization model of Euclidean distance matrices. We establish a non-asymptotic error bound for the random graph model with sub-Gaussian noise, and prove that our model produces a matrix estimator of high accuracy when the order of the uniform sample size is roughly the degree of freedom of a low-rank matrix up to a logarithmic factor. Our results partially explain why MVU and MVE often work well. Moreover, we develop a fast inexact accelerated proximal gradient method. Numerical experiments show that the model can produce configurations of high quality on large data points that the SDP approach would struggle to cope with.

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Section: Numerical Performancesupporting

confidence: 67%

Section: Embedding Methods In Manifold Learningmentioning

confidence: 88%

Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction

Ding

2016

Math. Program.

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“…Beyond that, analysis of the link prediction problem in a time series approach could help researchers gain a better understanding of the evolution of the networks. Many works have been done to study the dynamics of complex network [29][30][31]. The achievement of network prediction analysis could help explain the mechanism of the network evolution.…”

Section: Research Motivationmentioning

confidence: 99%

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

Gao

Musiał

Cooper

et al. 2015

Scientific Programming

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Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is theprediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

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“…It can be noticed that for directed graph optimal community structure includes more groups with bigger number of members than in the case of undirected graph. all vertices in the graph [4], [21]. The distribution of clustering coefficient is shown in Figure 8.…”

Section: ) Modularitymentioning

confidence: 99%

What kind of network are you?

Musiał

Gabrys

Buczko³

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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The amount of research done in the area of realworld networked systems is rapidly growing. Everybody knows what six degrees of separation or small-world phenomenon are. Scientists very easily give labels to the networks they analyse. If it has power law node degree distribution then it has to be scale-free network or if there is high clustering coefficient then it must be small-world network. These simplifications, although convenient, are not always very useful from the perspective of understanding phenomena existing within the network. In this paper we decided to go back to the basics and investigate whether analysis of one single measure is enough to describe a network. We analyse both local and global characteristics in order to discover the "true" nature of a network. Not only using local and/or global measures can lead to different classification of a network but we also show how significantly different interpretation can result from analysing the same data by building network models as directed/undirected and/or weighted/binary graphs.

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Molecular model of dynamic social network based on e-mail communication

Cited by 16 publications

References 36 publications

Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction

Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

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