Random graph theory provides a probabilistic framework for modelling and analysing networks in which connections between entities are assigned according to specified random processes. From its origins ...

Graph Random Neural Features (GRNF) is an embedding method from graph-structured data to real vectors based on a family of graph neural networks. GRNF can be used within traditional processing methods ...

Abstract: Random graphs, or more precisely the Erdős-Rényi random graph model, is a major tool for modeling complex networks. The most distinctive property of a random graph is inarguably the ...

Abstract: Distributed consensus computation over random graph processes is considered. The random graph process is defined as a sequence of random variables which take values from the set of all ...

Exponential random graph models (ERGMs) have emerged as a principal statistical framework for representing and analysing the formation of ties within social networks. By expressing the probability of ...

Graph Theory Algorithm is implemented in python. Jupyter Notebook is used to demonstrate the concept and Networkx library is used in several algorithms to visualize the graph.

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Birgitta Böckeler, Distinguished Engineer at ...

Fuzzy and random are two terms that seems and feels lot alike but are quite different. Many a times, we have been guilty of using it interchangeably in our daily life, which in my opinion is just fine ...

Random graphs are useful models of social and technological networks. To date, most of the research in this area has concerned geometric properties of the graphs. Here we focus on processes taking ...

We define a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. We provide ...