With the advent of a wide spectrum of recent applications, querying heterogeneous
information networks (HINs) has received a great deal of attention from both academic and …

Bipartite graphs are naturally used to model relationships between two different types of
entities, such as people-location, authorpaper, and customer-product. When modeling real …

An uncertain graph (also known as probabilistic graph) is a generic model to represent
many real-world networks from social to biological. In recent times, analysis and mining of …

Attributed network embedding seeks to depict each network node via a compact, low-
dimensional vector while effectively preserving the similarity between node pairs, which lays …

Despite the fact that SimRank has been successfully applied to various applications as a link-
based similarity measure, it suffers from a counter-intuitive property called a pairwise …

Numerous applications necessitate assessing the similarity between nodes across two
graphs using link structures. GSim, suggested by Blondel et al., is an appealing model …

Role-based similarity search, predicated on the topological structure of graphs, is a highly
effective and widely applicable technique for various real-world information extraction …

Graphs are widely used to represent interactions (ie, edges) between entities (ie,
nodes/vertices) in a large spectrum of applications, including social networks, biological …

With the advent of the Internet, graph-structured data are ubiquitous. An essential task for
graph-structured data management is similarity search based on graph topology, with a …