<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Failla, Andrea</style></author><author><style face="normal" font="default" size="100%">Citraro, Salvatore</style></author><author><style face="normal" font="default" size="100%">Rossetti, Giulio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Network Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">1–19</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Failla, Andrea</style></author><author><style face="normal" font="default" size="100%">Mazzoni, Federico</style></author><author><style face="normal" font="default" size="100%">Citraro, Salvatore</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Attribute-aware Community Events in Feature-rich Dynamic Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2022—Book of Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Failla, Andrea</style></author><author><style face="normal" font="default" size="100%">Citraro, Salvatore</style></author><author><style face="normal" font="default" size="100%">Rossetti, Giulio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Complex Networks and Their Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joseph, Simmi Marina</style></author><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Morini, Virginia</style></author><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Stella, Massimo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:2110.15269</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Letizia Milli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conformity: a Path-Aware Homophily measure for Node-Attributed Networks</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Intelligent SystemsIEEE Intelligent Systems</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE Intelligent Systems</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9321348</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1 - 1</style></pages><isbn><style face="normal" font="default" size="100%">1941-1294</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Letizia Milli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:2012.05195</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identifying and exploiting homogeneous communities in labeled networks</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Network Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">1–20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting EVA, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate EVA on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that EVA is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better EVA: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Letizia Milli</style></author><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Morini, Virginia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">UTLDR: an agent-based framework for modeling infectious diseases and public interventions</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:2011.05606</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A complex network approach to semantic spaces: How meaning organizes itself</style></title><secondary-title><style face="normal" font="default" size="100%">SEBD</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Salvatore Citraro</style></author><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Eva: Attribute-Aware Network Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Complex Networks and Their Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.</style></abstract></record></records></xml>