8 research outputs found
Network constraints on the mixing patterns of binary node metadata
We consider the network constraints on the bounds of the assortativity
coefficient, which measures the tendency of nodes with the same attribute
values to be interconnected. The assortativity coefficient is the Pearson's
correlation coefficient of node attribute values across network edges and
ranges between -1 and 1. We focus here on the assortativity of binary node
attributes and show that properties of the network, such as degree distribution
and the number of nodes with each attribute value place constraints upon the
attainable values of the assortativity coefficient. We explore the
assortativity in three different spaces, that is, ensembles of graph
configurations and node-attribute assignments that are valid for a given set of
network constraints. We provide means for obtaining bounds on the extremal
values of assortativity for each of these spaces. Finally, we demonstrate that
under certain conditions the network constraints severely limit the maximum and
minimum values of assortativity, which may present issues in how we interpret
the assortativity coefficient.Comment: 18 pages, 7 figure
Evaluating risks-based communities of Mafia companies: a complex networks perspective
This paper presents a data-driven complex network approach, to show similarities and differences-in terms of financial risks-between the companies involved in organized crime businesses and those who are not. At this aim, we construct and explore two networks under the assumption that highly connected companies hold similar financial risk profiles of large entity. Companies risk profiles are captured by a statistically consistent overall risk indicator, which is obtained by suitably aggregating four financial risk ratios. The community structures of the networks are analyzed under a statistical perspective, by implementing a rank-size analysis and by investigating the features of their distributions through entropic comparisons. The theoretical model is empirically validated through a high quality dataset of Italian companies. Results highlights remarkable differences between the considered sets of companies, with a higher heterogeneity and a general higher risk profiles in companies traceable back to a crime organization environment
Total variation based community detection using a nonlinear optimization approach
Maximizing the modularity of a network is a successful tool to identify an
important community of nodes. However, this combinatorial optimization problem
is known to be NP-complete. Inspired by recent nonlinear modularity eigenvector
approaches, we introduce the modularity total variation and show that
its box-constrained global maximum coincides with the maximum of the original
discrete modularity function. Thus we describe a new nonlinear optimization
approach to solve the equivalent problem leading to a community detection
strategy based on . The proposed approach relies on the use of a fast
first-order method that embeds a tailored active-set strategy. We report
extensive numerical comparisons with standard matrix-based approaches and the
Generalized RatioDCA approach for nonlinear modularity eigenvectors, showing
that our new method compares favourably with state-of-the-art alternatives
The Structure of U.S. College Networks on Facebook
Anecdotally, social connections made in university have life-long impact. Yet
knowledge of social networks formed in college remains episodic, due in large
part to the difficulty and expense involved in collecting a suitable dataset
for comprehensive analysis. To advance and systematize insight into college
social networks, we describe a dataset of the largest online social network
platform used by college students in the United States. We combine
de-identified and aggregated Facebook data with College Scorecard data,
campus-level information provided by U.S. Department of Education, to produce a
dataset covering the 2008-2015 entry year cohorts for 1,159 U.S. colleges and
universities, spanning 7.6 million students. To perform the difficult task of
comparing these networks of different sizes we develop a new methodology. We
compute features over sampled ego-graphs, train binary classifiers for every
pair of graphs, and operationalize distance between graphs as predictive
accuracy. Social networks of different year cohorts at the same school are
structurally more similar to one another than to cohorts at other schools.
Networks from similar schools have similar structures, with the public/private
and graduation rate dimensions being the most distinguishable. We also relate
school types to specific outcomes. For example, students at private schools
have larger networks that are more clustered and with higher homophily by year.
Our findings may help illuminate the role that colleges play in shaping social
networks which partly persist throughout people's lives.Comment: ICWSM-202
Network constraints on the mixing patterns of binary node metadata
We consider the network constraints on the bounds of the assortativity coefficient, which aims to quantify the tendency of nodes with the same attribute values to be connected. The assortativity coefficient can be considered as the Pearson’s correlation coefficient of node metadata values across network edges and lies in the interval [−1, 1]. However, properties of the network, such as degree distribution and the distribution of node metadata values place constraints upon the attainable values of the assortativity coefficient. This is important as a particular value of assortativity may say as much about the network topology as about how the metadata are distributed over the network – a fact often overlooked in literature where the interpretation tends to focus simply on the propensity of similar nodes to link to each other, without any regard on the constraints posed by the topology. In this paper we quantify the effect that the topology has on the assortativity coefficient in the case of binary node metadata. Specifically we look at the effect that the degree distribution, or the full topology, and the proportion of each metadata value has on the extremal values of the assortativity coefficient. We provide the means for obtaining bounds on the extremal values of assortativity for different settings and demonstrate that under certain conditions the maximum and minimum values of assortativity are severely limited, which may present issues in interpretation when these bounds are not considered
Network constraints on the mixing patterns of binary node metadata
We consider the network constraints on the bounds of the assortativity coefficient, which aims to quantify the tendency of nodes with the same attribute values to be connected. The assortativity coefficient can be considered as the Pearson's correlation coefficient of node metadata values across network edges and lies in the interval [-1,1]. However, properties of the network, such as degree distribution and the distribution of node metadata values, place constraints upon the attainable values of the assortativity coefficient. This is important as a particular value of assortativity may say as much about the network topology as about how the metadata are distributed over the network-a fact often overlooked in literature where the interpretation tends to focus simply on the propensity of similar nodes to link to each other, without any regard on the constraints posed by the topology. In this paper we quantify the effect that the topology has on the assortativity coefficient in the case of binary node metadata. Specifically, we look at the effect that the degree distribution, or the full topology, and the proportion of each metadata value has on the extremal values of the assortativity coefficient. We provide the means for obtaining bounds on the extremal values of assortativity for different settings and demonstrate that under certain conditions the maximum and minimum values of assortativity are severely limited, which may present issues in interpretation when these bounds are not considered