26,447 research outputs found
Improving Reachability and Navigability in Recommender Systems
In this paper, we investigate recommender systems from a network perspective
and investigate recommendation networks, where nodes are items (e.g., movies)
and edges are constructed from top-N recommendations (e.g., related movies). In
particular, we focus on evaluating the reachability and navigability of
recommendation networks and investigate the following questions: (i) How well
do recommendation networks support navigation and exploratory search? (ii) What
is the influence of parameters, in particular different recommendation
algorithms and the number of recommendations shown, on reachability and
navigability? and (iii) How can reachability and navigability be improved in
these networks? We tackle these questions by first evaluating the reachability
of recommendation networks by investigating their structural properties.
Second, we evaluate navigability by simulating three different models of
information seeking scenarios. We find that with standard algorithms,
recommender systems are not well suited to navigation and exploration and
propose methods to modify recommendations to improve this. Our work extends
from one-click-based evaluations of recommender systems towards multi-click
analysis (i.e., sequences of dependent clicks) and presents a general,
comprehensive approach to evaluating navigability of arbitrary recommendation
networks
Sampling properties of directed networks
For many real-world networks only a small "sampled" version of the original
network may be investigated; those results are then used to draw conclusions
about the actual system. Variants of breadth-first search (BFS) sampling, which
are based on epidemic processes, are widely used. Although it is well
established that BFS sampling fails, in most cases, to capture the
IN-component(s) of directed networks, a description of the effects of BFS
sampling on other topological properties are all but absent from the
literature. To systematically study the effects of sampling biases on directed
networks, we compare BFS sampling to random sampling on complete large-scale
directed networks. We present new results and a thorough analysis of the
topological properties of seven different complete directed networks (prior to
sampling), including three versions of Wikipedia, three different sources of
sampled World Wide Web data, and an Internet-based social network. We detail
the differences that sampling method and coverage can make to the structural
properties of sampled versions of these seven networks. Most notably, we find
that sampling method and coverage affect both the bow-tie structure, as well as
the number and structure of strongly connected components in sampled networks.
In addition, at low sampling coverage (i.e. less than 40%), the values of
average degree, variance of out-degree, degree auto-correlation, and link
reciprocity are overestimated by 30% or more in BFS-sampled networks, and only
attain values within 10% of the corresponding values in the complete networks
when sampling coverage is in excess of 65%. These results may cause us to
rethink what we know about the structure, function, and evolution of real-world
directed networks.Comment: 21 pages, 11 figure
Preferential attachment in the growth of social networks: the case of Wikipedia
We present an analysis of the statistical properties and growth of the free
on-line encyclopedia Wikipedia. By describing topics by vertices and hyperlinks
between them as edges, we can represent this encyclopedia as a directed graph.
The topological properties of this graph are in close analogy with that of the
World Wide Web, despite the very different growth mechanism. In particular we
measure a scale--invariant distribution of the in-- and out-- degree and we are
able to reproduce these features by means of a simple statistical model. As a
major consequence, Wikipedia growth can be described by local rules such as the
preferential attachment mechanism, though users can act globally on the
network.Comment: 4 pages, 4 figures, revte
Backbone of complex networks of corporations: The flow of control
We present a methodology to extract the backbone of complex networks based on
the weight and direction of links, as well as on nontopological properties of
nodes. We show how the methodology can be applied in general to networks in
which mass or energy is flowing along the links. In particular, the procedure
enables us to address important questions in economics, namely, how control and
wealth are structured and concentrated across national markets. We report on
the first cross-country investigation of ownership networks, focusing on the
stock markets of 48 countries around the world. On the one hand, our analysis
confirms results expected on the basis of the literature on corporate control,
namely, that in Anglo-Saxon countries control tends to be dispersed among
numerous shareholders. On the other hand, it also reveals that in the same
countries, control is found to be highly concentrated at the global level,
namely, lying in the hands of very few important shareholders. Interestingly,
the exact opposite is observed for European countries. These results have
previously not been reported as they are not observable without the kind of
network analysis developed here.Comment: 24 pages, 12 figures, 2nd version (text made more concise and
readable, results unchanged
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