@article {1484,
	title = {Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities},
	journal = {arXiv preprint arXiv:2110.15269},
	year = {2021},
	author = {Joseph, Simmi Marina and Salvatore Citraro and Morini, Virginia and Giulio Rossetti and Stella, Massimo}
}
@article {1429,
	title = {Conformity: a Path-Aware Homophily measure for Node-Attributed Networks},
	journal = {IEEE Intelligent SystemsIEEE Intelligent Systems},
	year = {2021},
	month = {2021},
	pages = {1 - 1},
	abstract = {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.},
	isbn = {1941-1294},
	doi = {https://doi.org/10.1109/MIS.2021.3051291},
	url = {https://ieeexplore.ieee.org/document/9321348},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@article {1364,
	title = {Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks},
	journal = {arXiv preprint arXiv:2012.05195},
	year = {2020},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@article {1358,
	title = {Identifying and exploiting homogeneous communities in labeled networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {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.},
	doi = {https://doi.org/10.1007/s41109-020-00302-1},
	url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@article {1367,
	title = {UTLDR: an agent-based framework for modeling infectious diseases and public interventions},
	journal = {arXiv preprint arXiv:2011.05606},
	year = {2020},
	author = {Giulio Rossetti and Letizia Milli and Salvatore Citraro and Morini, Virginia}
}
@conference {1379,
	title = {A complex network approach to semantic spaces: How meaning organizes itself},
	booktitle = {SEBD},
	year = {2019},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1357,
	title = {Eva: Attribute-Aware Network Segmentation},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	author = {Salvatore Citraro and Giulio Rossetti}
}
