277 research outputs found
Measuring the Generalized Friendship Paradox in Networks with Quality-dependent Connectivity
The friendship paradox is a sociological phenomenon stating that most people
have fewer friends than their friends do. The generalized friendship paradox
refers to the same observation for attributes other than degree, and it has
been observed in Twitter and scientific collaboration networks. This paper
takes an analytical approach to model this phenomenon. We consider a
preferential attachment-like network growth mechanism governed by both node
degrees and `qualities'. We introduce measures to quantify paradoxes, and
contrast the results obtained in our model to those obtained for an
uncorrelated network, where the degrees and qualities of adjacent nodes are
uncorrelated. We shed light on the effect of the distribution of node qualities
on the friendship paradox. We consider both the mean and the median to measure
paradoxes, and compare the results obtained by using these two statistics
Bursting activity spreading through asymmetric interactions
People communicate with those who have the same background or share a common
interest by using a social networking service (SNS). News or messages propagate
through inhomogeneous connections in an SNS by sharing or facilitating
additional comments. Such human activity is known to lead to endogenous
bursting in the rate of message occurrences. We analyze a multi-dimensional
self-exciting process to reveal dependence of the bursting activity on the
topology of connections and the distribution of interaction strength on the
connections. We determine the critical conditions for the cases where
interaction strength is regulated at either the point of input or output for
each person. In the input regulation condition, the network may exhibit
bursting with infinitesimal interaction strength, if the dispersion of the
degrees diverges as in the scale-free networks. In contrast, in the output
regulation condition, the critical value of interaction strength, represented
by the average number of events added by a single event, is a constant
, independent of the degree dispersion. Thus, the
stability in human activity crucially depends on not only the topology of
connections but also the manner in which interactions are distributed among the
connections.Comment: 8 pages, 8 figure
Benchmarking the Privacy-Preserving People Search
People search is an important topic in information retrieval. Many previous
studies on this topic employed social networks to boost search performance by
incorporating either local network features (e.g. the common connections
between the querying user and candidates in social networks), or global network
features (e.g. the PageRank), or both. However, the available social network
information can be restricted because of the privacy settings of involved
users, which in turn would affect the performance of people search. Therefore,
in this paper, we focus on the privacy issues in people search. We propose
simulating different privacy settings with a public social network due to the
unavailability of privacy-concerned networks. Our study examines the influences
of privacy concerns on the local and global network features, and their impacts
on the performance of people search. Our results show that: 1) the privacy
concerns of different people in the networks have different influences. People
with higher association (i.e. higher degree in a network) have much greater
impacts on the performance of people search; 2) local network features are more
sensitive to the privacy concerns, especially when such concerns come from high
association peoples in the network who are also related to the querying user.
As the first study on this topic, we hope to generate further discussions on
these issues.Comment: 4 pages, 5 figure
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