## Posts tagged ‘weighted-richclub’

### tnet: Software for Analysing Weighted Networks

*tnet* is a package written in *R* that can calculate weighted social network measures. Almost all of the ideas posted on this blog are related to weighted networks as, I believe, taking into consideration tie weights enables us to uncover and study interesting network properties. Not only are few social network measures applicable to weighted networks, but there is also a lack of software programmes that can analyse this type of networks. In fact, there are no open-source programmes. This hinders the use and development of weighted measures. *tnet* represents a first step towards creating such a programme. Through this platform, weighted network measures can easily be applied, and new measures easily implemented and distributed.

*tnet*manual, see Software.

### Weighted Rich-club Effect: A more appropriate null model for scientific collaboration networks

In this post, I extend the Weighted Rich-club Effect by suggesting and testing a different null model for the scientific collaboration network (Newman, 2001). This network is a two-mode network, which becomes an undirected one-mode network when projected. In the paper, we compared the observed weighted rich-club coefficient with the one found on random networks. The random networks were constructed by a null model defined for directed networks when prominence was based on node strength. Therefore, we created a directed network from the undirected scientific collaboration network by linking connected nodes with two directed ties that had the same weight. The null model consisted in reshuffling the tie weights attached to out-going ties for each node. However, this local reshuffling broke the weight symmetry of the two directed ties between connected nodes. The null model proposed in this post is based on the randomisation of the two-mode network before projecting it onto a one-mode network. By randomising before projecting, we are able to randomise a network while keeping the symmetry of weights.

*tnet*manual, see Weighted Rich-club Effect in Two-mode Networks.

### Thesis: Structure and Evolution of Weighted Networks

I have now completed my Ph.D. at the School of Business and Management of Queen Mary College, University of London. My Ph.D. programme was defined around a number of projects, which drew on, and extended, recent theoretical and methodological advances in network science. The projects that were concerned with weighted networks and longitudinal networks were outlined and critically discussed in my thesis (Structure and Evolution of Weighted Networks). The entire thesis, except Appendix C which is outdated, is available on the Publication > Thesis-page.

**Acknowledgements**

The theme of this thesis is interdependence among elements. In fact, this thesis is not just a product of myself, but also of my interdependence with others. Without the support of a number of people, it would not have been possible to write. It is my pleasure to have the opportunity to express my gratitude to many of them here.

For my academic achievements, I would like to acknowledge the constant support from my supervisors. In particular, I thank Pietro Panzarasa for taking an active part of all the projects I have worked on. I have also had the pleasure to collaborate with people other than my supervisors. I worked with Vittoria Colizza and Jose J. Ramasco on the analysis and method presented in Chapter 2, Kathleen M. Carley on an empirical analysis of the online social network used throughout this thesis, and Martha J. Prevezer on a project related to knowledge transfer in emerging countries. In addition to these direct collaborations, I would also like to thank Filip Agneessens, Sinan Aral, Steve Borgatti, Ronald Burt, Mauro Faccioni Filho, Thomas Friemel, John Skvoretz, and Vanina Torlo for encouragement and helpful advice. In particular, I would like to thank Tom A. B. Snijders and Klaus Nielsen for insightful reading of this thesis and many productive remarks and suggestions. I have also received feedback on my work at a number of conferences and workshops. I would like to express my gratitude to the participants at these.

On a social note, I would like to thank John, Claudius, and my family for their continuing support. Without them I would have lost focus. My peers and the administrative staff have also been a great source of support. In particular, I would like to extend my acknowledgements to Mariusz Jarmuzek, Geraldine Marks, Roland Miller, Jenny Murphy, Cathrine Seierstad, Lorna Soar, Steven Telford, and Eshref Trushin.

### Local weighted rich-club measure

This post proposes a local (node-level) version of the Weighted Rich-club Effect (PRL 101, 168702). By incorporating this measure into a regression analysis, the impact of targeting efforts towards prominent nodes on performance can be studied.

*tnet*manual, see The Weighted Rich-club Effect.