32,465 research outputs found
Individual position diversity in dependence socioeconomic networks increases economic output
The availability of big data recorded from massively multiplayer online
role-playing games (MMORPGs) allows us to gain a deeper understanding of the
potential connection between individuals' network positions and their economic
outputs. We use a statistical filtering method to construct dependence networks
from weighted friendship networks of individuals. We investigate the 30
distinct motif positions in the 13 directed triadic motifs which represent
microscopic dependences among individuals. Based on the structural similarity
of motif positions, we further classify individuals into different groups. The
node position diversity of individuals is found to be positively correlated
with their economic outputs. We also find that the economic outputs of leaf
nodes are significantly lower than that of the other nodes in the same motif.
Our findings shed light on understanding the influence of network structure on
economic activities and outputs in socioeconomic system.Comment: 19 pages, 5 figure
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
Quantifying Differential Privacy under Temporal Correlations
Differential Privacy (DP) has received increased attention as a rigorous
privacy framework. Existing studies employ traditional DP mechanisms (e.g., the
Laplace mechanism) as primitives, which assume that the data are independent,
or that adversaries do not have knowledge of the data correlations. However,
continuously generated data in the real world tend to be temporally correlated,
and such correlations can be acquired by adversaries. In this paper, we
investigate the potential privacy loss of a traditional DP mechanism under
temporal correlations in the context of continuous data release. First, we
model the temporal correlations using Markov model and analyze the privacy
leakage of a DP mechanism when adversaries have knowledge of such temporal
correlations. Our analysis reveals that the privacy leakage of a DP mechanism
may accumulate and increase over time. We call it temporal privacy leakage.
Second, to measure such privacy leakage, we design an efficient algorithm for
calculating it in polynomial time. Although the temporal privacy leakage may
increase over time, we also show that its supremum may exist in some cases.
Third, to bound the privacy loss, we propose mechanisms that convert any
existing DP mechanism into one against temporal privacy leakage. Experiments
with synthetic data confirm that our approach is efficient and effective.Comment: appears at ICDE 201
Patterns of link reciprocity in directed networks
We address the problem of link reciprocity, the non-random presence of two
mutual links between pairs of vertices. We propose a new measure of reciprocity
that allows the ordering of networks according to their actual degree of
correlation between mutual links. We find that real networks are always either
correlated or anticorrelated, and that networks of the same type (economic,
social, cellular, financial, ecological, etc.) display similar values of the
reciprocity. The observed patterns are not reproduced by current models. This
leads us to introduce a more general framework where mutual links occur with a
conditional connection probability. In some of the studied networks we discuss
the form of the conditional connection probability and the size dependence of
the reciprocity.Comment: Final version accepted for publication on Physical Review Letter
Entropy Rate of Diffusion Processes on Complex Networks
The concept of entropy rate for a dynamical process on a graph is introduced.
We study diffusion processes where the node degrees are used as a local
information by the random walkers. We describe analitically and numerically how
the degree heterogeneity and correlations affect the diffusion entropy rate. In
addition, the entropy rate is used to characterize complex networks from the
real world. Our results point out how to design optimal diffusion processes
that maximize the entropy for a given network structure, providing a new
theoretical tool with applications to social, technological and communication
networks.Comment: 4 pages (APS format), 3 figures, 1 tabl
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