id: 06067572
dt: a
an: 06067572
au: Spiegel, Stephan; Clausen, Jan; Albayrak, Sahin; Kunegis, Jérôme
ti: Link prediction on evolving data using tensor factorization.
so: Cao, Longbing (ed.) et al., New frontiers in applied data mining. PAKDD
    2011 international workshops, Shenzhen, China, May 24‒27, 2011.
    Revised selected papers. Berlin: Springer (ISBN 978-3-642-28319-2/pbk).
    Lecture Notes in Computer Science 7104. Lecture Notes in Artificial
    Intelligence, 100-110 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: link prediction algorithm; temporal network analysis; evolving data;
    multi-way array; tensor factorization
ci: 
li: doi:10.1007/978-3-642-28320-8_9
ab: Summary: Within the last few years a lot of research has been done on large
    social and information networks. One of the principal challenges
    concerning complex networks is link prediction. Most link prediction
    algorithms are based on the underlying network structure in terms of
    traditional graph theory. In order to design efficient algorithms for
    large scale networks, researchers increasingly adapt methods from
    advanced matrix and tensor computations. This paper proposes a novel
    approach of link prediction for complex networks by means of multi-way
    tensors. In addition to structural data we furthermore consider
    temporal evolution of a network. Our approach applies the canonical
    Parafac decomposition to reduce tensor dimensionality and to retrieve
    latent trends. For the development and evaluation of our proposed link
    prediction algorithm we employed various popular datasets of online
    social networks like Facebook and Wikipedia. Our results show
    significant improvements for evolutionary networks in terms of
    prediction accuracy measured through mean average precision.
rv: