Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

Given a large graph, how can we summarize it with fewer nodes and edges while
maintaining its key properties, eg node degrees and graph spectrum? As a solution, graph …

Large-scale, highly-interconnected networks pervade our society and the natural world
around us, including the World Wide Web, social networks, knowledge graphs, genome and …

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn
task-independent, low-dimensional, and continuous vectors for nodes preserving both …

Weights on the edges of a graph can show interactions among members of a social network,
emails exchanged in any organization, and traffic flow on roads. However, mining hidden …

Graph summarization is a useful tool for analyzing large-scale graphs. Some works tried to
preserve original node embeddings encoding rich structural information of nodes on the …

In large dynamic graphs, it is often impractical to store and process the entire graph. To
contend with such graphs, lossless graph summarization is a compression technique that …

Graphs are used in various applications and to model real world objects. To understand the
underlying characteristics of large graphs, graph summarization becomes a hot topic aiming …

Community detection is an important content in complex network analysis. The existing
community detection methods in attributed networks mostly focus on only using network …

Graphs are widely used in various applications, and their size is becoming larger over the
passage of time. It is necessary to reduce their size to minimize main memory needs and to …