2 research outputs found
Estimating Formation Mechanisms and Degree Distributions in Mixed Attachment Networks
Our work introduces an approach for estimating the contribution of
attachment mechanisms to the formation of growing networks. We present a generic
model in which growth is driven by the continuous attachment of new nodes according
to random and preferential linkage with a xed probability. Past approaches apply
likelihood analysis to estimate the probability of occurrence of each mechanism at
a particular network instance, exploiting the concavity of the likelihood function at
each point in time. However, the probability of connecting to existing nodes, and
consequently the likelihood function itself, varies as networks grow. We establish
conditions under which applying likelihood analysis guarantees the existence of a
local maximum of the time-varying likelihood function and prove that an expectation
maximization algorithm provides a convergent estimate. Furthermore, the in-degree
distributions of the nodes in the growing networks is analytically characterized.
Simulations show that, under the proposed conditions, expectation maximization and
maximum-likelihood accurately estimate the actual contribution of each mechanism,
and in-degree distributions converge to stationary distributions