Graph partitioning is an important preprocessing step to distributed graph processing. In
edge partitioning, the edge set of a given graph is split into $ k $ equally-sized partitions …

We study a random graph G n that combines certain aspects of geometric random graphs
and preferential attachment graphs. The vertices of G n are n sequentially generated points …

Autonomous object counting system is of great use in retail stores, industries and also in
research processes. In this paper, a Speeded Up Robust Feature (SURF) based robust …

We survey the recent work on phase transition and distances in various random graph
models with general degree sequences. We focus on inhomogeneous random graphs, the …

Graph models for real-world complex networks such as the Internet, the WWW and
biological networks are necessary for simulation-based studies of a variety of problems …

Synchronization in random networks with given expected degree sequences is studied. We
also investigate in details the synchronization in networks whose topology is described by …

The study of random graphs has traditionally been dominated by the closely-related models
G (n, m), in which a graph is sampled from the uniform distribution on graphs with n vertices …

Preferential attachment (PA) models of network structure are widely used due to their
explanatory power and conceptual simplicity. PA models are able to account for the scale …

Many social and biological networks consist of communities–groups of nodes within which
links are dense but among which links are sparse. It turns out that most of these networks …

In this paper a discrete-time dynamic random graph process is studied that interleaves the
birth of nodes and edges with the death of nodes. In this model, at each time step either a …