56,410 research outputs found
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
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia
Hyperlinks are an essential feature of the World Wide Web. They are
especially important for online encyclopedias such as Wikipedia: an article can
often only be understood in the context of related articles, and hyperlinks
make it easy to explore this context. But important links are often missing,
and several methods have been proposed to alleviate this problem by learning a
linking model based on the structure of the existing links. Here we propose a
novel approach to identifying missing links in Wikipedia. We build on the fact
that the ultimate purpose of Wikipedia links is to aid navigation. Rather than
merely suggesting new links that are in tune with the structure of existing
links, our method finds missing links that would immediately enhance
Wikipedia's navigability. We leverage data sets of navigation paths collected
through a Wikipedia-based human-computation game in which users must find a
short path from a start to a target article by only clicking links encountered
along the way. We harness human navigational traces to identify a set of
candidates for missing links and then rank these candidates. Experiments show
that our procedure identifies missing links of high quality
- …