Doubly threshold graphs for social network modeling

Threshold graphs are recursive deterministic network models that capture properties of certain social and economic interactions. One drawback of these graph families is that they they have limited constrained generative attachment rules. To mitigate this problem, we introduce a new class of graphs termed Doubly Threshold (DT) graphs which may be succinctly described through vertex weights that govern the existence of edges via two inequalities. One inequality imposes the constraint that the sum of weights of adjacent vertices has to exceed a specified threshold. The second inequality ensures that adjacent vertices have a bounded difference of their weights. We provide a succinct characterization and decomposition of DT graphs and analyze their forbidden induced subgraphs which we compare to those of known social networks. We also present a method for performing vertex weight assignments on DT graphs that satisfy the defining constraints.