With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Graph embedding algorithms embed a graph into a vector space where the structure and
the inherent properties of the graph are preserved. The existing graph embedding methods …

Attributed network embedding aims to seek low-dimensional vector representations for
nodes in a network, such that original network topological structure and node attribute …

Network embedding leverages the node proximity manifested to learn a low-dimensional
node vector representation for each node in the network. The learned embeddings could …

Data embedding is used in many machine learning applications to create low-dimensional
feature representations, which preserves the structure of data points in their original space …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …

The increasing growth of online social networks has drawn researchers' attention to link
prediction and has been adopted in many fields, including computer sciences, information …