Vectorised Spreading Activation algorithm for centrality measurement

TROUSSOV, A., DARENA, F., eIZKA, J., PARRA, D., BRUSILOVSKY, P.: Vectorised Spreading Activation Algorithm for Centrality Measurement. Spreading Activation is a family of graph-based alg orithms 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, i teratively 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 central ity measurement in accordance with dynamic model-based view of centrality that focuses on the outcomes for nodes in a network where something is flowing from node to node across the e dges. Representing the activation by vectors allows the use of the information about various dim ensionalities of the flow and the dynamic of the flow. In this capacity, VSA algorithms can mode l multitude of complex multidimensional network flows. We present the results of numerical simulations on small synthetic social network and multidimensional 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 traditi onal SA algorithms. We tentatively conclude that the VSA methods could be instrumental to devel op scalable and computationally efficient algorithms which could achieve synergy between computation of centrality indexes with detection of community structures in networks. Base d 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 t heir applications to centrality measurement.