Complex social and information network search becomes important with a variety of
applications. In the core of these applications, lies a common and critical problem: Given a …

It is increasingly common to find real-life data represented as networks of labeled,
heterogeneous entities. To query these networks, one often needs to identify the matches of …

Since many graph data are often noisy and incomplete in real applications, it has become
increasingly important to retrieve graphs g in the graph database D that approximately …

Due to many real applications of graph databases, it has become increasingly important to
retrieve graphs g (in graph database D) that approximately match with query graph q, rather …

Querying similar graphs in graph databases has been widely studied in graph query
processing in recent years. Existing works mainly focus on subgraph similarity search and …

Graph pattern matching is fundamental to social network analysis. Traditional techniques
are subgraph isomorphism and graph simulation. However, these notions often impose too …

The graph structure is a very important means to model schemaless data with complicated
structures, such as protein-protein interaction networks, chemical compounds, knowledge …

Estimating similarity between vertices is a fundamental issue in network analysis across
various domains, such as social networks and biological networks. Methods based on …

Large graph datasets are common in many emerging database applications, and most
notably in large-scale scientific applications. To fully exploit the wealth of information …

Graphs are widely used to model complex data in many applications, such as bioinformatics,
chemistry, social networks, pattern recognition, etc. A fundamental and critical query …