Posts tagged ‘valued networks’

Are triangles made up by strong ties?

Are strong ties embedded?A key assumption of Granovetter’s (1973) Strength of Weak Ties theory is that strong ties are embedded by being part of triangles, whereas weak ties are not embedded by being created towards disconnected nodes. This assumption have been tested by calculating the traditional clustering coefficient on binary networks created with increasing cut-off parameters (i.e., creating a series of binary networks from a weighted network where ties with a weight greater than a cut-off parameter is set to present and the rest removed). Contrarily to theories of strong ties and embeddedness, these methods generally showed that the clustering coefficient decreased as the cut-off parameter increased. However, the binary networks were not comparable with each other as they had a different number of ties. Another way of testing this assumption is to take the ratio between the weighted global clustering coefficient and the traditional coefficient measured on networks where all ties are considered present. Thus, the number of ties is maintained. This post highlights this feature and empirically tests it on a number of publically available weighted network datasets.

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

April 17, 2009 at 12:00 am

Article: Clustering in Weighted Networks

TripletA paper called “Clustering in Weighted Networks” that I have co-authored will be published in Social Networks. Although many social network measures exist for binary networks and many theories differentiate between strong and weak ties, few measures have been generalised so that they can be applied to weighted networks and retain the information encoded in the weights of ties. One of these measures is the global clustering coefficient, which measures embeddedness or, more specifically, the likelihood of a triplet being closed by a tie so that it forms a triangle. This article proposes a generalisation of this key network measure to weighted networks.

Continue Reading April 3, 2009 at 12:00 am 7 comments

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

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