Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Such temporal networks (or edge streams) consist of a sequence of …

Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …

The success of many graph-based machine learning tasks highly depends on an
appropriate representation learned from the graph data. Most work has focused on learning …

Visualization recommendation work has focused solely on scoring visualizations based on
the underlying dataset, and not the actual user and their past visualization feedback. These …

Due to the data sparsity problem, social network information is often additionally used to
improve the performance of recommender systems. While most existing works exploit social …

Matrix completion (MC) with additional information has found wide applicability in several
machine learning applications. Among algorithms for solving such problems, Inductive …

Stochastic gradient descent (SGD) is widely used by many machine learning algorithms. It is
efficient for big data ap-plications due to its low algorithmic complexity. SGD is inherently …

In recent years, quantization methods have successfully accelerated the training of large
deep neural network (DNN) models by reducing the level of precision in computing …

Recommender systems for online broadcasting become important as the number of
channels has been increasing. In online broadcasting, to provide accurate recommendation …

The purpose of this study is to investigate the hybrid parallelization of the Stochastic
Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high …