Likelihood-based approach to discriminate mixtures of network models that vary in time

Abstract

Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

References in corpus (12)

• Emergence of scaling in random networks
• Assortative mixing in networks
• Clustering and preferential attachment in growing networks
• Universal Behavior of Load Distribution in Scale-free Networks
• Connectivity of Growing Random Networks
• Growing networks with local rules: preferential attachment, clustering hierarchy and degree correlations
• Motifs in Temporal Networks
• Are randomly grown graphs really random?
• Evolution of reference networks with aging
• Scale-free network growth by ranking
• Detection of timescales in evolving complex systems
• Identifying time dependence in network growth

Cited by in corpus (2)

• Power Laws, the Price Model, and the Pareto type-2 Distribution
• Scale-Invariant Strength Assortativity of Streaming Butterflies