KERNEL MODELS FOR COMPLEX NETWORKS

Abstract

We advocate the study of complex network models based on kernel functions. These are hybrids between macroscopic random graph models for networks with heavy tailed statistics (or other desired structural characteristics), and microscopic models that incorporate network semantics. In particular, kernel-based models assign explicit semantics to network elements (nodes and links), while they maintain the conceptual and implementational simplicity of random graph models.