The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of training data. This paper considers a variety of types of domain knowledge for constraining parameter estimates when learning Bayesian Networks. In particular, we consider domain knowledge that constrains the values or ...

Diffusion processes in networks are increasingly used to model dynamic phenomena such as the spread of information, wildlife, or social influence. Our work addresses the problem of learning the underlying parameters that govern such a diffusion process by observing the time at which nodes become active. A key advantage of our approach is that, unlike previous work, it can tolerate missing obser...

The energy conservation of lossless network reflects a series of novel symmetry in S parameter. This paper presents the generalized modulus symmetry, spurious reciprocity, constant characteristic phase and determinant of the lossless block network. The perfect matching condition of block load network [Γl] and the invariable lossless property of S parameter of generalized block network are devel...

We developed Græmlin 2.0, a new multiple network aligner with (1) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (2) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adap...