7,756 research outputs found
The power of indirect social ties
While direct social ties have been intensely studied in the context of
computer-mediated social networks, indirect ties (e.g., friends of friends)
have seen little attention. Yet in real life, we often rely on friends of our
friends for recommendations (of good doctors, good schools, or good
babysitters), for introduction to a new job opportunity, and for many other
occasional needs. In this work we attempt to 1) quantify the strength of
indirect social ties, 2) validate it, and 3) empirically demonstrate its
usefulness for distributed applications on two examples. We quantify social
strength of indirect ties using a(ny) measure of the strength of the direct
ties that connect two people and the intuition provided by the sociology
literature. We validate the proposed metric experimentally by comparing
correlations with other direct social tie evaluators. We show via data-driven
experiments that the proposed metric for social strength can be used
successfully for social applications. Specifically, we show that it alleviates
known problems in friend-to-friend storage systems by addressing two previously
documented shortcomings: reduced set of storage candidates and data
availability correlations. We also show that it can be used for predicting the
effects of a social diffusion with an accuracy of up to 93.5%.Comment: Technical Repor
Analysis of complex contagions in random multiplex networks
We study the diffusion of influence in random multiplex networks where links
can be of different types, and for a given content (e.g., rumor, product,
political view), each link type is associated with a content dependent
parameter in that measures the relative bias type- links
have in spreading this content. In this setting, we propose a linear threshold
model of contagion where nodes switch state if their "perceived" proportion of
active neighbors exceeds a threshold \tau. Namely, a node connected to
active neighbors and inactive neighbors via type- links will turn
active if exceeds its threshold \tau. Under this
model, we obtain the condition, probability and expected size of global
spreading events. Our results extend the existing work on complex contagions in
several directions by i) providing solutions for coupled random networks whose
vertices are neither identical nor disjoint, (ii) highlighting the effect of
content on the dynamics of complex contagions, and (iii) showing that
content-dependent propagation over a multiplex network leads to a subtle
relation between the giant vulnerable component of the graph and the global
cascade condition that is not seen in the existing models in the literature.Comment: Revised 06/08/12. 11 Pages, 3 figure
Data clustering using a model granular magnet
We present a new approach to clustering, based on the physical properties of
an inhomogeneous ferromagnet. No assumption is made regarding the underlying
distribution of the data. We assign a Potts spin to each data point and
introduce an interaction between neighboring points, whose strength is a
decreasing function of the distance between the neighbors. This magnetic system
exhibits three phases. At very low temperatures it is completely ordered; all
spins are aligned. At very high temperatures the system does not exhibit any
ordering and in an intermediate regime clusters of relatively strongly coupled
spins become ordered, whereas different clusters remain uncorrelated. This
intermediate phase is identified by a jump in the order parameters. The
spin-spin correlation function is used to partition the spins and the
corresponding data points into clusters. We demonstrate on three synthetic and
three real data sets how the method works. Detailed comparison to the
performance of other techniques clearly indicates the relative success of our
method.Comment: 46 pages, postscript, 15 ps figures include
Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures
Formal Concept Analysis "FCA" is a data analysis method which enables to
discover hidden knowledge existing in data. A kind of hidden knowledge
extracted from data is association rules. Different quality measures were
reported in the literature to extract only relevant association rules. Given a
dataset, the choice of a good quality measure remains a challenging task for a
user. Given a quality measures evaluation matrix according to semantic
properties, this paper describes how FCA can highlight quality measures with
similar behavior in order to help the user during his choice. The aim of this
article is the discovery of Interestingness Measures "IM" clusters, able to
validate those found due to the hierarchical and partitioning clustering
methods "AHC" and "k-means". Then, based on the theoretical study of sixty one
interestingness measures according to nineteen properties, proposed in a recent
study, "FCA" describes several groups of measures.Comment: 13 pages, 2 figure
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