01602nas a2200133   4500008004100000245005600041210005600097520113100153100002301284700001901307700002501326700002401351856009301375       2017                        eng d00aEfficiently Clustering Very Large Attributed Graphs0 aEfficiently Clustering Very Large Attributed Graphs3 aAttributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.1 aBaroni, Alessandro1 aConte, Alessio1 aPatrignani, Maurizio1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/efficiently-clustering-very-large-attributed-graphs01350nas a2200133   4500008004100000022001400041245005900055210005900114260000800173520094200181100002301123700002401146856004601170       2017                        eng d  a1573-767500aSegregation discovery in a social network of companies0 aSegregation discovery in a social network of companies  cSep3 aWe introduce a framework for the data-driven analysis of social segregation of minority groups, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem is introduced, which consists of searching sub-groups of population and minorities for which a segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem by computing a multi-dimensional data cube that can be explored by the analyst. The machinery underlying the search algorithm relies on frequent itemset mining concepts and tools. The framework is challenged on a cases study in the context of company networks. We analyse segregation on the grounds of sex and age for directors in the boards of the Italian companies. The network includes 2.15M companies and 3.63M directors.1 aBaroni, Alessandro1 aRuggieri, Salvatore  uhttps://doi.org/10.1007/s10844-017-0485-001127nas a2200121   4500008004100000245005900041210005900100260001900159520069200178100002300870700002400893856008800917       2015                        eng d00aSegregation Discovery in a Social Network of Companies0 aSegregation Discovery in a Social Network of Companies  bSpringer, Cham3 aWe introduce a framework for a data-driven analysis of segregation of minority groups in social networks, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem consists of searching sub-graphs and sub-groups for which a reference segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem. The framework is challenged on the analysis of segregation of social groups in the boards of directors of the real and large network of Italian companies connected through shared directors.1 aBaroni, Alessandro1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/segregation-discovery-social-network-companies