Higher-order hierarchical spectral clustering for multidimensional data: Brandi G., Di Matteo T. (2021) Higher-Order Hierarchical Spectral Clustering for Multidimensional Data. In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_31

TY - CHAP

T1 - Higher-order hierarchical spectral clustering for multidimensional data

T2 - Brandi G., Di Matteo T. (2021) Higher-Order Hierarchical Spectral Clustering for Multidimensional Data. In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_31

AU - Di Matteo, Tiziana

PY - 2021

Y1 - 2021

N2 - Understanding the community structure of countries in the international food network is of great importance for policymakers. In- deed, clusters might be the key for the understanding of the geopolitical and economic interconnectedness between countries. Their detection and analysis might lead to a bona fide evaluation of the impact of spillover effects between countries in situations of distress. In this paper, we intro- duce a clustering methodology that we name Higher-order Hierarchical Spectral Clustering (HHSC), which combines an higher order tensor fac- torization and a hierarchical clustering algorithm. We apply this method- ology to a multidimensional system of countries and products involved in the import-export trade network (FAO dataset). We find a structural proxy of countries interconnectedness that is not only valid for a specific product but for the whole trade system. We retrieve clusters which are linked to economic activity and geographical proximity.

AB - Understanding the community structure of countries in the international food network is of great importance for policymakers. In- deed, clusters might be the key for the understanding of the geopolitical and economic interconnectedness between countries. Their detection and analysis might lead to a bona fide evaluation of the impact of spillover effects between countries in situations of distress. In this paper, we intro- duce a clustering methodology that we name Higher-order Hierarchical Spectral Clustering (HHSC), which combines an higher order tensor fac- torization and a hierarchical clustering algorithm. We apply this method- ology to a multidimensional system of countries and products involved in the import-export trade network (FAO dataset). We find a structural proxy of countries interconnectedness that is not only valid for a specific product but for the whole trade system. We retrieve clusters which are linked to economic activity and geographical proximity.

M3 - Conference paper

BT - Higher-Order Hierarchical Spectral Clustering for Multidimensional Data.

ER -