Posts tagged ‘weighted networks’

The importance of allowing ties to decay

Evolving networkRecently, a number of network dataset have been constructed from archival data (e.g., email logs) with the aim to study human interaction. This has allowed researchers to study large-scale social networks. If the archival data does not included information about the severing or weakening of ties, non-relevant interaction among people, which occurred far in the past, might be deemed relevant. This post highlights this issue and suggests imposing a lifespan on interactions to record only relevant ties with the current strength.

The content of this post has been integrated in the tnet manual, see Sliding Window.

March 20, 2009 at 12:00 am

Article: Patterns and Dynamics of Users’ Behaviour and Interaction: Network Analysis of an Online Community

Online Social NetworkA paper called “Patterns and Dynamics of Users’ Behaviour and Interaction: Network Analysis of an Online Community” that I have co-authored will be published in the Journal of the American Society for Information Science and Technology (JASIST). In this paper, we studied the evolution of a variety of properties in an online community, including how users create, reciprocate, and deepen relationships with one another, variations in users’ gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the community.

Continue Reading March 6, 2009 at 12:00 am 2 comments

Betweenness in weighted networks

Betweenness exampleThis post highlights a generalisation of Freeman’s (1978) betweenness measure to weighted networks implicitly introduced by Brandes (2001) when he developed an algorithm for calculating betweenness faster. Betweenness is a measure of the extent to which a node funnels transactions among all the other nodes in the network. By funnelling the transactions, a node can broker. This could be by taking a cut (e.g. Ukraine controls most gas pipelines from Russia to Europe) or distorting the information being transmitted to its advantage.

The content of this post has been integrated in the tnet manual, see Node Centrality in Weighted Networks.

February 20, 2009 at 12:00 am 6 comments

Operationalisation of tie strength in social networks

Ties' strength and weightThe method used to operationalise ties’ strength into weights affects the outcomes of weighted networks measures. Simply assigning 1, 2, and 3 to three different levels of tie strength might not be appropriate as this scale might misrepresent the actually difference among the three levels (using an ordinal scale). In this post, I highlight issues with collecting weighted social network data from surveys.

The content of this post has been integrated in the tnet manual, see Defining Weighted Networks.

February 6, 2009 at 12:00 am 1 comment

Weighted local clustering coefficient

TripletThe generalisation of the local clustering coefficient to weighted networks by Barrat et al. (2004) considers the value of a triplet to be the average of the weights attached to the two ties that make up the triplet. In this post, I suggest three additional methods for defining the triplet value.
 
 

The content of this post has been integrated in the tnet manual, see Clustering in Weighted Networks.

January 23, 2009 at 12:00 am 1 comment

Average shortest distance in weighted networks

Distance between nodesThe average distance that separate nodes in a network became a famous measure following Milgram’s six-degrees of separation experiment in 1967 that found that people in the US were on average 6-steps from each other. This post proposes a generalisation of this measure to weighted networks by building on work by Dijkstra (1959) and Newman (2001).

The content of this post has been integrated in the tnet manual, see Shortest Paths in Weighted Networks.

January 9, 2009 at 12:00 am 8 comments

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.

The content of this post has been integrated in the tnet manual, see The Weighted Rich-club Effect.

December 26, 2008 at 12:31 am 1 comment

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