Mostly wrapper functions that can be used in conjunction with indirect_relations to fine tune indirect relations.



dist_dpow(x, alpha = 1)

dist_powd(x, alpha = 0.5)


walks_exp(x, alpha = 1)

walks_exp_even(x, alpha = 1)

walks_exp_odd(x, alpha = 1)

walks_attenuated(x, alpha = 1/max(x) * 0.99)

walks_uptok(x, alpha = 1, k = 3)



Matrix of relations.


Potential weighting factor.


For walk counts up to a certain length.


Transformed relations as matrix


The predefined functions follow the naming scheme relation_transformation. Predefined functions walks_* are thus best used with type="walks" in indirect_relations. Theoretically, however, any transformation can be used with any relation. The results might, however, not be interpretable.

The following functions are implemented so far: dist_2pow returns \(2^{-x}\) dist_inv returns \(1/x\) dist_dpow returns \(x^{-\alpha}\) where \(\alpha\) should be chosen greater than 0. dist_powd returns \(\alpha^x\) where \(\alpha\) should be chosen between 0 and 1. walks_limit_prop returns the limit proportion of walks between pairs of nodes. Calculating rowSums of this relation will result in the principle eigenvector of the network. walks_exp returns \(\sum_{k=0}^\infty \frac{A^k}{k!}\) walks_exp_even returns \(\sum_{k=0}^\infty \frac{A^{2k}}{(2k)!}\) walks_exp_odd returns \(\sum_{k=0}^\infty \frac{A^{2k+1}}{(2k+1)!}\) walks_attenuated returns \(\sum_{k=0}^\infty \alpha^k A^k\) walks_uptok returns \(\sum_{j=0}^k \alpha^j A^j\)

Walk based transformation are defined on the eigen decomposition of the adjacency matrix using the fact that $$f(A)=Xf(\Lambda)X^T.$$ Care has to be taken when using user defined functions.