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2021
Journal article
Open Access
X-Mark: a benchmark for node-attributed community discovery algorithms
Citraro S., Rossetti G.
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2
DOI: 10.1007/s13278-021-00823-2
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Rossetti G.
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2
DOI: 10.1007/s13278-021-00823-2
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com 
| Social Network Analysis and Mining | Social Network Analysis and Mining | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Unveiling 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 have 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. Different 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.Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
DOI: 10.1109/mis.2021.3051291
DOI: 10.48550/arxiv.2012.05195
Project(s): SoBigData-PlusPlus
Metrics:
Rossetti G., Citraro S., Milli L.
Unveiling 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 have 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. Different 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.Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
DOI: 10.1109/mis.2021.3051291
DOI: 10.48550/arxiv.2012.05195
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | IEEE Intelligent Systems | ieeexplore.ieee.org | IEEE Intelligent Systems | ISTI Repository | doi.org 
| CNR ExploRA