10,367 research outputs found
Link creation and profile alignment in the aNobii social network
The present work investigates the structural and dynamical properties of
aNobii\footnote{http://www.anobii.com/}, a social bookmarking system designed
for readers and book lovers. Users of aNobii provide information about their
library, reading interests and geographical location, and they can establish
typed social links to other users. Here, we perform an in-depth analysis of the
system's social network and its interplay with users' profiles. We describe the
relation of geographic and interest-based factors to social linking.
Furthermore, we perform a longitudinal analysis to investigate the interplay of
profile similarity and link creation in the social network, with a focus on
triangle closure. We report a reciprocal causal connection: profile similarity
of users drives the subsequent closure in the social network and, reciprocally,
closure in the social network induces subsequent profile alignment. Access to
the dynamics of the social network also allows us to measure quantitative
indicators of preferential linking.Comment: http://www.iisocialcom.org/conference/socialcom2010
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
Topological network alignment uncovers biological function and phylogeny
Sequence comparison and alignment has had an enormous impact on our
understanding of evolution, biology, and disease. Comparison and alignment of
biological networks will likely have a similar impact. Existing network
alignments use information external to the networks, such as sequence, because
no good algorithm for purely topological alignment has yet been devised. In
this paper, we present a novel algorithm based solely on network topology, that
can be used to align any two networks. We apply it to biological networks to
produce by far the most complete topological alignments of biological networks
to date. We demonstrate that both species phylogeny and detailed biological
function of individual proteins can be extracted from our alignments.
Topology-based alignments have the potential to provide a completely new,
independent source of phylogenetic information. Our alignment of the
protein-protein interaction networks of two very different species--yeast and
human--indicate that even distant species share a surprising amount of network
topology with each other, suggesting broad similarities in internal cellular
wiring across all life on Earth.Comment: Algorithm explained in more details. Additional analysis adde
Link Prediction in Complex Networks: A Survey
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure
Global Networks of Trade and Bits
Considerable efforts have been made in recent years to produce detailed
topologies of the Internet. Although Internet topology data have been brought
to the attention of a wide and somewhat diverse audience of scholars, so far
they have been overlooked by economists. In this paper, we suggest that such
data could be effectively treated as a proxy to characterize the size of the
"digital economy" at country level and outsourcing: thus, we analyse the
topological structure of the network of trade in digital services (trade in
bits) and compare it with that of the more traditional flow of manufactured
goods across countries. To perform meaningful comparisons across networks with
different characteristics, we define a stochastic benchmark for the number of
connections among each country-pair, based on hypergeometric distribution.
Original data are thus filtered by means of different thresholds, so that we
only focus on the strongest links, i.e., statistically significant links. We
find that trade in bits displays a sparser and less hierarchical network
structure, which is more similar to trade in high-skill manufactured goods than
total trade. Lastly, distance plays a more prominent role in shaping the
network of international trade in physical goods than trade in digital
services.Comment: 25 pages, 6 figure
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