Belief propagation for networks with loops
https://advances.sciencemag.org/content/7/17/eabf1211.abstract

> Our method operates by dividing a network into neighborhoods (20).  A  
> neighborhood  Ni(r)  around  node i  is  defined  as  the  node  iitself and 
> all of its edges and neighboring nodes, plus all nodes and edges along paths 
> of length r or less between the neighbors of i. See Fig. 1 for examples. The 
> key to our approach is to focus initially on networks  in  which  there  are  
> no  paths  longer  than  r  between  the  neighbors of i, meaning that all 
> paths are inside Ni(r).  This  means that all correlations between spins 
> within Ni(r) are accounted for by edges that are also within Ni(r), which 
> allows us to write exact mes-sage passing equations for these networks. 
> Equivalently, we can de-fine a primitive cycle of length r starting at node i 
> to be a cycle (i.e., a self-avoiding loop) such that at least one edge in the 
> cycle is not on any shorter cycle beginning and ending at i. Our methods are 
> then exact on any network that contains no primitive cycles of length greater 
> than r + 2.
> ...
> Having defined the initial neighborhood Ni, we further define a neighborhood 
> Nj  ∖  i  to  be  node  j  plus  all  edges  in  Nj  that  are  not  
> contained in Ni and the nodes at their ends. Our method involves writing the 
> marginal probability distribution on the spin at node i in terms of a set of 
> messages received from nodes j that are in Ni, in-cluding nodes that are not 
> immediate neighbors of i. (This contrasts with  traditional  message  passing 
>  in  which  messages  are  received  only from the immediate neighbors of i.) 
> These messages are then, in turn, calculated from further messages j receives 
> from nodes k∈Nj ∖ i and so forth.


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