EAN: 9789811989339

SPRINGERBRIEFS IN COMPUTER SCIENCE DYNAMIC NE...

A dynamic network is frequently encountered in various real industrial applications  such as
the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time
interactions among them  where each node indicates a specified entity  each directed link
indicates a real-time interaction  and the strength of an interaction can be quantified as the
weight of a link. As the involved nodes increase drastically  it becomes impossible to observe
their full interactions at each time slot  making a resultant dynamic network High Dimensional
and Incomplete (HDI). An HDI dynamic network with directed and weighted links  despite its HDI
nature  contains rich knowledge regarding involved nodes' various behavior patterns. Therefore
it is essential to study how to build efficient and effective representation learning models
for acquiring useful knowledge. In this book  we first model a dynamic network into an HDI
tensor and present the basic latent factorization of tensors (LFT) model. Then  we propose four
representative LFT-based network representation methods. The first method integrates the
short-time bias  long-time bias and preprocessing bias to precisely represent the volatility of
network data. The second method utilizes a proportion-al-integral-derivative controller to
construct an adjusted instance error to achieve a higher convergence rate. The third method
considers the non-negativity of fluctuating network data by constraining latent features to be
non-negative and incorporating the extended linear bias. The fourth method adopts an
alternating direction method of multipliers framework to build a learning model for
implementing representation to dynamic networks with high preciseness and efficiency.

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