Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Vectorised Spreading Activation algorithm for centrality measurement

Troussov, Alexander and Dařena, František and Žižka, Jan and Parra-Santander, Denis and Brusilovsky, Peter (2011) Vectorised Spreading Activation algorithm for centrality measurement. Acta Universitatis Agriculturae et Silvicultura. Mendelianae Brunensis, 59 (7). 469 - 476.

Published Version
Available under License : See the attached license file.

Download (777kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


Spreading Activation is a family of graph-based algorithms widely used in areas such as information retrieval, epidemic models, and recommender systems. In this paper we introduce a novel Spreading Activation (SA) method that we call Vectorised Spreading Activation (VSA). VSA algorithms, like “traditional” SA algorithms, iteratively propagate the activation from the initially activated set of nodes to the other nodes in a network through outward links. The level of the node’s activation could be used as a centrality measurement in accordance with dynamic model-based view of centrality that focuses on the outcomes for nodes in a network where something is fl owing from node to node across the edges. Representing the activation by vectors allows the use of the information about various dimensionalities of the fl ow and the dynamic of the fl ow. In this capacity, VSA algorithms can model multitude of complex multidimensional network fl ows. We present the results of numerical simulations on small synthetic social networks and multi dimensional network models of folksonomies which show that the results of VSA propagation are more sensitive to the positions of the initial seed and to the community structure of the network than the results produced by traditional SA algorithms. We tentatively conclude that the VSA methods could be instrumental to develop scalable and computationally effi cient algorithms which could achieve synergy between computation of centrality indexes with detection of community structures in networks. Based on our preliminary results and on improvements made over previous studies, we foresee advances and applications in the current state of the art of this family of algorithms and their applications to centrality measurement.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Troussov, Alexander
Dařena, František
Žižka, Jan
Parra-Santander, Denis
Brusilovsky, Peterpeterb@pitt.eduPETERB0000-0002-1902-1464
Date: July 2011
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Acta Universitatis Agriculturae et Silvicultura. Mendelianae Brunensis
Volume: 59
Number: 7
Page Range: 469 - 476
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Article Type: Research Article
Date Deposited: 03 Aug 2012 20:02
Last Modified: 01 Nov 2017 12:57


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item