8 research outputs found
Hidden Variables in Bipartite Networks
We introduce and study random bipartite networks with hidden variables. Nodes
in these networks are characterized by hidden variables which control the
appearance of links between node pairs. We derive analytic expressions for the
degree distribution, degree correlations, the distribution of the number of
common neighbors, and the bipartite clustering coefficient in these networks.
We also establish the relationship between degrees of nodes in original
bipartite networks and in their unipartite projections. We further demonstrate
how hidden variable formalism can be applied to analyze topological properties
of networks in certain bipartite network models, and verify our analytical
results in numerical simulations
The Problem of Action at a Distance in Networks and the Emergence of Preferential Attachment from Triadic Closure
In this paper, we characterise the notion of preferential attachment in
networks as action at a distance, and argue that it can only be an emergent
phenomenon -- the actual mechanism by which networks grow always being the
closing of triangles. After a review of the concepts of triangle closing and
preferential attachment, we present our argument, as well as a simplified model
in which preferential attachment can be derived mathematically from triangle
closing. Additionally, we perform experiments on synthetic graphs to
demonstrate the emergence of preferential attachment in graph growth models
based only on triangle closing.Comment: 13 pages, three figure
Latent Geometry for Complementarity-Driven Networks
Networks of interdisciplinary teams, biological interactions as well as food
webs are examples of networks that are shaped by complementarity principles:
connections in these networks are preferentially established between nodes with
complementary properties. We propose a geometric framework for
complementarity-driven networks. In doing so we first argue that traditional
geometric representations, e.g., embeddings of networks into latent metric
spaces, are not applicable to complementarity-driven networks due to the
contradiction between the triangle inequality in latent metric spaces and the
non-transitivity of complementarity. We then propose the cross-geometric
representation for these complementarity-driven networks and demonstrate that
this representation (i) follows naturally from the complementarity rule, (ii)
is consistent with the metric property of the latent space, (iii) reproduces
structural properties of real complementarity-driven networks, if the latent
space is the hyperbolic disk, and (iv) allows for prediction of missing links
in complementarity-driven networks with accuracy surpassing existing
similarity-based methods. The proposed framework challenges social network
analysis intuition and tools that are routinely applied to
complementarity-driven networks and offers new avenues towards descriptive and
prescriptive analysis of systems in science of science and biomedicine
Clustering coefficients for networks with higher order interactions
We introduce a clustering coefficient for nondirected and directed
hypergraphs, which we call the quad clustering coefficient. We determine the
average quad clustering coefficient and its distribution in real-world
hypergraphs and compare its value with those of random hypergraphs drawn from
the configuration model. We find that clustering in real-world hypergraphs is
significantly different from those of random hypergraphs. Notably, we find that
real-world hypergraphs exhibit a nonnegligible fraction of nodes with a maximal
value of the quad clustering coefficient, while we do not find such nodes in
random hypergraphs. Moreover, these highly clustered nodes are not observed in
an analysis based on the pairwise clustering coefficient of the associated
projected graph that has binary interactions, and hence higher order
interactions are required to identify nodes with a large quad clustering
coefficient.Comment: 29 pages, 18 figure
Clustering coefficients for networks with higher order interactions
We introduce a clustering coefficient for nondirected and directed hypergraphs, which we call the quad clustering coefficient. We determine the average quad clustering coefficient and its distribution in real-world hypergraphs and compare its value with those of random hypergraphs drawn from the configuration model. We find that real-world hypergraphs exhibit a nonnegligible fraction of nodes with a maximal value of the quad clustering coefficient, while we do not find such nodes in random hypergraphs. Interestingly, these highly clustered nodes can have large degrees and can be incident to hyperedges of large cardinality. Moreover, highly clustered nodes are not observed in an analysis based on the pairwise clustering coefficient of the associated projected graph that has binary interactions, and hence higher order interactions are required to identify nodes with a large quad clustering coefficient
Randomizing bipartite networks: The case of the World Trade Web
This is the final version. Available from Nature Research via the DOI in this record. Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization.GROWTHCO
Social Data Mining for Crime Intelligence
With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems