Network Models & Graph Generation

← Back to Domain-Specific Families

Abstractions about generating and computing over network structures, including random and recursively generated graphs, graph neural networks, Petri nets for concurrency, and graph invariants like Shannon capacity.

6 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• Evolutionary acquisition of neural topologies — An evolutionary reinforcement-learning method that jointly evolves artificial-neural-network structure and weights, using structural mutation and evolution-strategy parameter optimization.
• Graph neural network — A neural-network family that learns node, edge or whole-graph representations by repeatedly aggregating relational information in a permutation-aware graph structure.
• Kronecker graph — A recursively generated graph whose adjacency matrix is formed by repeated Kronecker products of a small initiator matrix, producing large self-similar network structure from few parameters.
• Petri net — A bipartite place-transition graph with a token marking whose enabled transition firings consume and produce tokens, modeling concurrency, synchronization and resource flow in discrete-event systems.
• Random graph — A graph-valued random object specified by a probability distribution or stochastic generation rule over vertices and edges.
• Shannon capacity of a graph — A graph invariant giving the asymptotic zero-error information rate of a confusability graph under repeated independent channel uses.