Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graph embedding based on random-walks supports effective solutions for many graph-
related downstream tasks. However, the abundance of embedding literature has made it …

Click-through rate (CTR) prediction is one of the most central tasks in online advertising
systems. Recent deep learning-based models that exploit feature embedding and high …

Presently with technology node scaling, an accurate prediction model at early design stages
can significantly reduce the design cycle. Especially during logic synthesis, predicting cell …

Low-dimensional embeddings, from classical spectral embeddings to modern neural-net-
inspired methods, are a cornerstone in the modeling and analysis of complex networks …

Node embedding learns a low-dimensional representation for each node in the graph.
Recent progress on node embedding shows that proximity matrix factorization methods gain …

Contrastive learning has emerged as a premier method for learning representations with or
without supervision. Recent studies have shown its utility in graph representation learning …

Graph representation learning methods, such as node embeddings, are powerful
approaches to map nodes into a latent vector space, allowing their use for various graph …

While most network embedding techniques model the relative positions of nodes in a
network, recently there has been significant interest in structural embeddings that model …

Label spreading is a general technique for semi-supervised learning with point cloud or
network data, which can be interpreted as a diffusion of labels on a graph. While there are …