Graph data represent information about entities (vertices) and the relationships or connections between them (edges). In real-world networks today, new data are constantly being produced, leading to the notion of dynamic ...

Graph analysis can be used to study streaming data from a variety of sources, such as social networks, financial transactions, and online communication. The analysis of streaming data poses many challenges, including dealing ...

Agglomerative Clustering techniques work by recursively merging graph vertices into communities, to maximize a clustering quality metric. The metric of Modularity coined by Newman and Girvan, measures the cluster quality ...

Many of today's massive and rapidly changing graphs contain internal structure---hierarchies of locally dense regions---and finding and tracking this structure is key to detecting emerging behavior, exposing internal activity, ...

Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many ...