Database Reference
In-Depth Information
recent approaches in literature access have boosted the social context of biblio-
graphic resources and bind their quality to the importance of corresponding authors
on scientist social networks. The social network of authors is a generalized represen-
tation of the coauthorship and citation network that possibly integrates additional
entities and interactions. Unlike early work that models the social network of authors
by using coauthorship associations only [ 41 , 48 ], recent works include documents as
information nodes in the social network and align entities into document and author
layers with possible associations connecting nodes from different layers [ 18 ]. These
models extract social relationships from interactions involving document nodes such
as the collaboration, the publication, and the citation.
The social importance of authors is evaluated by a set of measures introduced by
both domains of social network analysis [ 49 ] and hyperlink analysis [ 46 , 47 ]. In the
context of scientific publications, the Betweeness measure is considered as an
indicator of interdisciplinarity and highlights authors connecting dispersed parti-
tions of the scientific community. The Closeness measure, based on the shortest
path in the graph, reflects the reachability and independence of an author in his
social neighborhood. The PageRank measure and the Authority score computed
by the HITS algorithm distinguish the authoritative resources in the social network.
By contrast, the Hub score computed by the HITS algorithm identifies authors
having an important social activity and relying on authoritative resources, and
these authors are called Centrals .
With the introduction of academic social networks on the Web (e.g., C ITE U LIKE 3
and A CADEMIA 4 ), the importance of scientific papers is inferred not only from its
production context but also through its consuming context. The social network of
bibliographic resources is extended so as to include more social entities interacting
in the social producing and consuming context of the document. It includes all the
actors and the data that help to estimate the social relevance of documents. In fact,
actors represent information producers (authors) and information consumers
(users), whereas data cover documents and social annotations (tags, rating,
reviews). Accordingly, actors become information nodes collaborating to produce
documents and interacting to provide social annotations. In this context, the impor-
tance of the scientific paper is estimated by the social importance of related actors,
as well as their popularity and received tags [ 21 ].
6.4 Research Objectives and Contributions
Our focus in this chapter is on the formalization of a social information retrieval,
gathering several entity types that share and exchange information. More specifically,
we instantiate the generic model within a scientific community and show how to
model scientific information retrieval embedded within authors, users, and taggers.
3 http://www.citeulike.org
4 http://www.academia.edu/
Search WWH ::




Custom Search