328 research outputs found
Triadic motifs and dyadic self-organization in the World Trade Network
In self-organizing networks, topology and dynamics coevolve in a continuous
feedback, without exogenous driving. The World Trade Network (WTN) is one of
the few empirically well documented examples of self-organizing networks: its
topology strongly depends on the GDP of world countries, which in turn depends
on the structure of trade. Therefore, understanding which are the key
topological properties of the WTN that deviate from randomness provides direct
empirical information about the structural effects of self-organization. Here,
using an analytical pattern-detection method that we have recently proposed, we
study the occurrence of triadic "motifs" (subgraphs of three vertices) in the
WTN between 1950 and 2000. We find that, unlike other properties, motifs are
not explained by only the in- and out-degree sequences. By contrast, they are
completely explained if also the numbers of reciprocal edges are taken into
account. This implies that the self-organization process underlying the
evolution of the WTN is almost completely encoded into the dyadic structure,
which strongly depends on reciprocity.Comment: 12 pages, 3 figures; Best Paper Award at the 6th International
Conference on Self-Organizing Systems, Delft, The Netherlands, 15-16/03/201
Early-warning signals of topological collapse in interbank networks
The financial crisis clearly illustrated the importance of characterizing the
level of 'systemic' risk associated with an entire credit network, rather than
with single institutions. However, the interplay between financial distress and
topological changes is still poorly understood. Here we analyze the quarterly
interbank exposures among Dutch banks over the period 1998-2008, ending with
the crisis. After controlling for the link density, many topological properties
display an abrupt change in 2008, providing a clear - but unpredictable -
signature of the crisis. By contrast, if the heterogeneity of banks'
connectivity is controlled for, the same properties show a gradual transition
to the crisis, starting in 2005 and preceded by an even earlier period during
which anomalous debt loops could have led to the underestimation of
counter-party risk. These early-warning signals are undetectable if the network
is reconstructed from partial bank-specific data, as routinely done. We discuss
important implications for bank regulatory policies.Comment: 28 pages, 23 figures, 1 tabl
Stationarity, non-stationarity and early warning signals in economic networks
Economic integration, globalization and financial crises represent examples
of processes whose understanding requires the analysis of the underlying
network structure. Of particular interest is establishing whether a real
economic network is in a state of (quasi)stationary equilibrium, i.e.
characterized by smooth structural changes rather than abrupt transitions.
While in the former case the behaviour of the system can be reasonably
controlled and predicted, in the latter case this is generally impossible.
Here, we propose a method to assess whether a real economic network is in a
quasi-stationary state by checking the consistency of its structural evolution
with appropriate quasi-equilibrium maximum-entropy ensembles of graphs. As
illustrative examples, we consider the International Trade Network (ITN) and
the Dutch Interbank Network (DIN). We find that the ITN is an almost perfect
example of quasi-equilibrium network, while the DIN is clearly
out-of-equilibrium. In the latter, the entity of the deviation from
quasi-stationarity contains precious information that allows us to identify
remarkable early warning signals of the interbank crisis of 2008. These early
warning signals involve certain dyadic and triadic topological properties,
including dangerous 'debt loops' with different levels of interbank
reciprocity.Comment: 12 pages, 9 figures. Extended version of the paper "Economic networks
in and out of equilibrium" (arXiv:1309.1875
Clustering in complex networks. I. General formalism
We develop a full theoretical approach to clustering in complex networks. A
key concept is introduced, the edge multiplicity, that measures the number of
triangles passing through an edge. This quantity extends the clustering
coefficient in that it involves the properties of two --and not just one--
vertices. The formalism is completed with the definition of a three-vertex
correlation function, which is the fundamental quantity describing the
properties of clustered networks. The formalism suggests new metrics that are
able to thoroughly characterize transitive relations. A rigorous analysis of
several real networks, which makes use of the new formalism and the new
metrics, is also provided. It is also found that clustered networks can be
classified into two main groups: the {\it weak} and the {\it strong
transitivity} classes. In the first class, edge multiplicity is small, with
triangles being disjoint. In the second class, edge multiplicity is high and so
triangles share many edges. As we shall see in the following paper, the class a
network belongs to has strong implications in its percolation properties
Reconstructing networks
Complex networks datasets often come with the problem of missing information:
interactions data that have not been measured or discovered, may be affected by
errors, or are simply hidden because of privacy issues. This Element provides
an overview of the ideas, methods and techniques to deal with this problem and
that together define the field of network reconstruction. Given the extent of
the subject, we shall focus on the inference methods rooted in statistical
physics and information theory. The discussion will be organized according to
the different scales of the reconstruction task, that is, whether the goal is
to reconstruct the macroscopic structure of the network, to infer its mesoscale
properties, or to predict the individual microscopic connections.Comment: 107 pages, 25 figure
Reconstructing networks
Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections
Triadic Motifs in the Partitioned World Trade Web
AbstractOne of the crucial aspects of the Internet of Things that influences the effectiveness of communication among devices is the communication model, for which no universal solution exists. The actual interaction pattern can in general be represented as a directed graph, whose nodes represent the "Things" and whose directed edges represent the sent messages. Frequent patterns can identify channels or infrastructures to be strengthened and can help in choosing the most suitable message routing schema or network protocol. In general, frequent patterns have been called motifs and overrepresented motifs have been recognized to be the low-level building blocks of networks and to be useful to explain many of their properties, playing a relevant role in determining their dynamic and evolution. In this paper triadic motifs are found first partitioning a network by strength of connections and then analyzing the partitions separately. The case study is the World Trade Web (WTW), that is the directed graph connecting world Countries with trade relationships, with the aim of finding its topological characterization in terms of motifs and isolating the key factors underlying its evolution. The WTW has been split based on the weights of the graph to highlight structural differences between the big players in terms of volumes of trade and the rest of the world. As test case, the period 2003-2010 has been analyzed, to show the structural effect of the economical crisis in the year 2007
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Structural balance emerges and explains performance in risky decision-making.
Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize
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