In the first sentence of the abstract or the introduction (of my bibliography).
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios.
Large attributed graphs are ubiquitous in many application domains, such as traffic analysis and planning, network analysis, and bioinformatics.
The traditional computer-to-computer communication networks have been evolving to include the ubiquitously connected device-centric networks.
Graphs are now ubiquitous in almost every field of research.
Nowadays relational information is ubiquitous and complex structures are required to manage and mine this data.
Graphs are ubiquitous structures that capture relationships (edges) among entities (nodes).
Multilayer networks are formed by several networks that evolve and interact with each other. These networks are ubiquitous and include social networks, financial markets, multimodal transportation systems, infrastructures, climate networks, ecological networks, molecular networks and the brain.
Graphs are ubiquitous for representing complex relationships between individuals or objects and are often used to model social interactions, energy grids, computer networks, brain connectivity, etc.
Graphs are among the most ubiquitous models in computer science with an array of applications in drug discovery (Takigawa & Mamitsuka (2013)), biological protein-protein networks (Sun et al. (2017)), data representation and organization (Gross & Yellen (2005)), recommendation systems (Kutty et al. (2014)), and social networks (Nettleton (2013)).