@article{1461295,
      recid = {1461295},
      author = {Wu, Hao, and Wu, Xuke, and Luo, Xin,},
      title = {Dynamic network representation based on latent  factorization of tensors /},
      pages = {1 online resource (viii, 80 pages) :},
      abstract = {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.},
      url = {http://library.usi.edu/record/1461295},
      doi = {https://doi.org/10.1007/978-981-19-8934-6},
}