Clustering of tag-induced sub-graphs in complex networks

We study the behavior of the clustering coefficient in tagged networks. The rich variety of tags associated with the nodes in the studied systems provide additional information about the entities represented by the nodes which can be important for practical applications like searching in the networks. Here we examine how the clustering coefficient changes when narrowing the network to a sub-graph marked by a given tag, and how does it correlate with various other properties of the sub-graph. Another interesting question addressed in the paper is how the clustering coefficient of the individual nodes is affected by the tags on the node. We believe these sort of analysis help acquiring a more complete description of the structure of large complex systems

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries in the data, SAM aims at recovering full causal models from continuous observational data along a multivariate non-parametric setting. The approach is based on a game between $d$ players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the overall joint conditional distribution, and that of the original data. An original learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the end-to-end optimization of the graph structure and parameters through stochastic gradient descent. Besides the theoretical analysis of the approach in the large sample limit, SAM is extensively experimentally validated on synthetic and real data

Dispersion relations and wave operators in self-similar quasicontinuous linear chains

We construct self-similar functions and linear operators to deduce a self-similar variant of the Laplacian operator and of the D'Alembertian wave operator. The exigence of self-similarity as a symmetry property requires the introduction of nonlocal particle-particle interactions. We derive a self-similar linear wave operator describing the dynamics of a quasicontinuous linear chain of infinite length with a spatially self-similar distribution of nonlocal interparticle springs. The self-similarity of the nonlocal harmonic particle-particle interactions results in a dispersion relation of the form of a Weierstrass-Mandelbrot function that exhibits self-similar and fractal features. We also derive a continuum approximation, which relates the self-similar Laplacian to fractional integrals, and yields in the low-frequency regime a power-law frequency-dependence of the oscillator density

Put three and three together: Triangle-driven community detection

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to quantify the quality of a community work well in general, under some circumstances, they fail at correctly capturing such notion. The main reason is that these metrics consider the internal community edges as a set, but ignore how these actually connect the vertices of the community. We propose the Weighted Community Clustering (WCC), which is a new community metric that takes the triangle instead of the edge as the minimal structural motif indicating the presence of a strong relation in a graph. We theoretically analyse WCC in depth and formally prove, by means of a set of properties, that the maximization of WCC guarantees communities with cohesion and structure. In addition, we propose Scalable Community Detection (SCD), a community detection algorithm based on WCC, which is designed to be fast and scalable on SMP machines, showing experimentally that WCC correctly captures the concept of community in social networks using real datasets. Finally, using ground-truth data, we show that SCD provides better quality than the best disjoint community detection algorithms of the state of the art while performing faster.Peer ReviewedPostprint (author's final draft

Wage Dispersion in a Partially Unionized Labor Force

Taking as our point of departure a model proposed by David Card (2001), we suggest new methods for analyzing wage dispersion in a partially unionized labor market. Card's method disaggregates the labor population into skill categories, which procedure entails some loss of information. Accordingly, we develop a model in which each worker individually is assigned a union-membership probability and predicted union and nonunion wages. The model yields a natural three-way decomposition of variance. The decomposition permits counterfactual analysis, using concepts and techniques from the theory of factorial experimental design. We examine causes of the increase in U.K. wage dispersion between 1983 and 1995. Of the factors initially considered, the most influential was a change in the structure of remuneration inside both the union and nonunion sectors. Next in importance was the decrease in union membership. Finally, exogenous changes in labor force characteristics had, for most groups considered, only a small negative effect. We supplement this preliminary three-factorial analysis with a five-factorial analysis that allows us to examine effects from the wage-equation parameters in greater detail.wage dispersion, three-way variance decomposition, bivariate kernel density smoothing, union membership, deunionization, factorial experimental design

Long-term impacts of tropical storms and earthquakes on human population growth in Haiti and the Dominican Republic

Since the 18th century, Haiti and the Dominican Republic have experienced similar natural forces, including earthquakes and tropical storms. These countries are two of the most prone of all Latin American and Caribbean countries to natural hazards events, while Haiti seems to be more vulnerable to natural forces. This article discusses to what extent geohazards have shaped both nation's demographic developments. The data show that neither atmospheric nor seismic forces that directly hit the territory of Haiti have significantly affected the country's population growth rates and spatial population densities. Conversely, since the 1950s more people were exposed to atmospheric hazards, in particular, in regions which historically experienced higher storm frequencies

Characterizing the community structure of complex networks

Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as ``fingerprints'' of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Our findings are verified by the use of two fundamentally different community detection methods.Comment: 15 pages, 20 figures, 4 table