11,781 research outputs found
Static and Dynamic Aspects of Scientific Collaboration Networks
Collaboration networks arise when we map the connections between scientists
which are formed through joint publications. These networks thus display the
social structure of academia, and also allow conclusions about the structure of
scientific knowledge. Using the computer science publication database DBLP, we
compile relations between authors and publications as graphs and proceed with
examining and quantifying collaborative relations with graph-based methods. We
review standard properties of the network and rank authors and publications by
centrality. Additionally, we detect communities with modularity-based
clustering and compare the resulting clusters to a ground-truth based on
conferences and thus topical similarity. In a second part, we are the first to
combine DBLP network data with data from the Dagstuhl Seminars: We investigate
whether seminars of this kind, as social and academic events designed to
connect researchers, leave a visible track in the structure of the
collaboration network. Our results suggest that such single events are not
influential enough to change the network structure significantly. However, the
network structure seems to influence a participant's decision to accept or
decline an invitation.Comment: ASONAM 2012: IEEE/ACM International Conference on Advances in Social
Networks Analysis and Minin
Concentric Characterization and Classification of Complex Network Nodes: Theory and Application to Institutional Collaboration
Differently from theoretical scale-free networks, most of real networks
present multi-scale behavior with nodes structured in different types of
functional groups and communities. While the majority of approaches for
classification of nodes in a complex network has relied on local measurements
of the topology/connectivity around each node, valuable information about node
functionality can be obtained by Concentric (or Hierarchical) Measurements. In
this paper we explore the possibility of using a set of Concentric Measurements
and agglomerative clustering methods in order to obtain a set of functional
groups of nodes. Concentric clustering coefficient and convergence ratio are
chosen as segregation parameters for the analysis of a institutional
collaboration network including various known communities (departments of the
University of S\~ao Paulo). A dendogram is obtained and the results are
analyzed and discussed. Among the interesting obtained findings, we emphasize
the scale-free nature of the obtained network, as well as the identification of
different patterns of authorship emerging from different areas (e.g. human and
exact sciences). Another interesting result concerns the relatively uniform
distribution of hubs along the concentric levels, contrariwise to the
non-uniform pattern found in theoretical scale free networks such as the BA
model.Comment: 15 pages, 13 figure
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Clicking data, which exists in abundance and contains objective user
preference information, is widely used to produce personalized recommendations
in web-based applications. Current popular recommendation algorithms, typically
based on matrix factorizations, often have high accuracy and achieve good
clickthrough rates. However, diversity of the recommended items, which can
greatly enhance user experiences, is often overlooked. Moreover, most
algorithms do not produce interpretable uncertainty quantifications of the
recommendations. In this work, we propose the Bayesian Mallows for Clicking
Data (BMCD) method, which augments clicking data into compatible full ranking
vectors by enforcing all the clicked items to be top-ranked. User preferences
are learned using a Mallows ranking model. Bayesian inference leads to
interpretable uncertainties of each individual recommendation, and we also
propose a method to make personalized recommendations based on such
uncertainties. With a simulation study and a real life data example, we
demonstrate that compared to state-of-the-art matrix factorization, BMCD makes
personalized recommendations with similar accuracy, while achieving much higher
level of diversity, and producing interpretable and actionable uncertainty
estimation.Comment: 27 page
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