412 research outputs found
Self-organized criticality in a model of collective bank bankruptcies
The question we address here is of whether phenomena of collective
bankruptcies are related to self-organized criticality. In order to answer it
we propose a simple model of banking networks based on the random directed
percolation. We study effects of one bank failure on the nucleation of
contagion phase in a financial market. We recognize the power law distribution
of contagion sizes in 3d- and 4d-networks as an indicator of SOC behavior. The
SOC dynamics was not detected in 2d-lattices. The difference between 2d- and
3d- or 4d-systems is explained due to the percolation theory.Comment: For Int. J. Mod. Phys. C 13, No. 3, six pages including four figure
The Structure of Information Pathways in a Social Communication Network
Social networks are of interest to researchers in part because they are
thought to mediate the flow of information in communities and organizations.
Here we study the temporal dynamics of communication using on-line data,
including e-mail communication among the faculty and staff of a large
university over a two-year period. We formulate a temporal notion of "distance"
in the underlying social network by measuring the minimum time required for
information to spread from one node to another -- a concept that draws on the
notion of vector-clocks from the study of distributed computing systems. We
find that such temporal measures provide structural insights that are not
apparent from analyses of the pure social network topology. In particular, we
define the network backbone to be the subgraph consisting of edges on which
information has the potential to flow the quickest. We find that the backbone
is a sparse graph with a concentration of both highly embedded edges and
long-range bridges -- a finding that sheds new light on the relationship
between tie strength and connectivity in social networks.Comment: 9 pages, 10 figures, to appear in Proceedings of the 14th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining (KDD'08),
August 24-27, 2008, Las Vegas, Nevada, US
Information dynamics shape the networks of Internet-mediated prostitution
Like many other social phenomena, prostitution is increasingly coordinated
over the Internet. The online behavior affects the offline activity; the
reverse is also true. We investigated the reported sexual contacts between
6,624 anonymous escorts and 10,106 sex-buyers extracted from an online
community from its beginning and six years on. These sexual encounters were
also graded and categorized (in terms of the type of sexual activities
performed) by the buyers. From the temporal, bipartite network of posts, we
found a full feedback loop in which high grades on previous posts affect the
future commercial success of the sex-worker, and vice versa. We also found a
peculiar growth pattern in which the turnover of community members and sex
workers causes a sublinear preferential attachment. There is, moreover, a
strong geographic influence on network structure-the network is geographically
clustered but still close to connected, the contacts consistent with the
inverse-square law observed in trading patterns. We also found that the number
of sellers scales sublinearly with city size, so this type of prostitution does
not, comparatively speaking, benefit much from an increasing concentration of
people
On the Topological Properties of the World Trade Web: A Weighted Network Analysis
This paper studies the topological properties of the World Trade Web (WTW)
and its evolution over time by employing a weighted network analysis. We show
that the WTW, viewed as a weighted network, displays statistical features that
are very different from those obtained by using a traditional binary-network
approach. In particular, we find that: (i) the majority of existing links are
associated to weak trade relationships; (ii) the weighted WTW is only weakly
disassortative; (iii) countries holding more intense trade relationships are
more clustered.Comment: To be submitted to APFA 6 Proceedings. 8 pages, 10 figure
Community Aliveness: Discovering Interaction Decay Patterns in Online Social Communities
Online Social Communities (OSCs) provide a medium for connecting people,
sharing news, eliciting information, and finding jobs, among others. The
dynamics of the interaction among the members of OSCs is not always growth
dynamics. Instead, a or dynamics often
happens, which makes an OSC obsolete. Understanding the behavior and the
characteristics of the members of an inactive community help to sustain the
growth dynamics of these communities and, possibly, prevents them from being
out of service. In this work, we provide two prediction models for predicting
the interaction decay of community members, namely: a Simple Threshold Model
(STM) and a supervised machine learning classification framework. We conducted
evaluation experiments for our prediction models supported by a of decayed communities extracted from the StackExchange platform. The
results of the experiments revealed that it is possible, with satisfactory
prediction performance in terms of the F1-score and the accuracy, to predict
the decay of the activity of the members of these communities using
network-based attributes and network-exogenous attributes of the members. The
upper bound of the prediction performance of the methods we used is and
for the F1-score and the accuracy, respectively. These results indicate
that network-based attributes are correlated with the activity of the members
and that we can find decay patterns in terms of these attributes. The results
also showed that the structure of the decayed communities can be used to
support the alive communities by discovering inactive members.Comment: pre-print for the 4th European Network Intelligence Conference -
11-12 September 2017 Duisburg, German
The Dynamics of a Mobile Phone Network
The empirical study of network dynamics has been limited by the lack of
longitudinal data. Here we introduce a quantitative indicator of link
persistence to explore the correlations between the structure of a mobile phone
network and the persistence of its links. We show that persistent links tend to
be reciprocal and are more common for people with low degree and high
clustering. We study the redundancy of the associations between persistence,
degree, clustering and reciprocity and show that reciprocity is the strongest
predictor of tie persistence. The method presented can be easily adapted to
characterize the dynamics of other networks and can be used to identify the
links that are most likely to survive in the future
The structure of information pathways in a social communication network
Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate a temporal notion of âdistance â in the underlying social network by measuring the minimum time required for information to spread from one node to another â a concept that draws on the notion of vector-clocks from the study of distributed computing systems. We find that such temporal measures provide structural insights that are not apparent from analyses of the pure social network topology. In particular, we define the network backbone to be the subgraph consisting of edges on which information has the potential to flow the quickest. We find that the backbone is a sparse graph with a concentration of both highly embedded edges and long-range bridges â a finding that sheds new light on the relationship between tie strength and connectivity in social networks
Complex scale-free networks with tunable power-law exponent and clustering
This article is made available through the Brunel Open Access Publishing Fund. It is distributed under a Creative Commons License (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2013 Elsevier B.V.We introduce a network evolution process motivated by the network of citations in the scientific literature. In each iteration of the process a node is born and directed links are created from the new node to a set of target nodes already in the network. This set includes mm âambassadorâ nodes and ll of each ambassadorâs descendants where mm and ll are random variables selected from any choice of distributions plpl and qmqm. The process mimics the tendency of authors to cite varying numbers of papers included in the bibliographies of the other papers they cite. We show that the degree distributions of the networks generated after a large number of iterations are scale-free and derive an expression for the power-law exponent. In a particular case of the model where the number of ambassadors is always the constant mm and the number of selected descendants from each ambassador is the constant ll, the power-law exponent is (2l+1)/l(2l+1)/l. For this example we derive expressions for the degree distribution and clustering coefficient in terms of ll and mm. We conclude that the proposed model can be tuned to have the same power law exponent and clustering coefficient of a broad range of the scale-free distributions that have been studied empirically.EPSR
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