  Most existing classical artificial neural networks (ANN) are designed as a
tree structure to imitate neural networks. In this paper, we argue that the
connectivity of a tree is not sufficient to characterize a neural network. The
nodes of the same level of a tree cannot be connected with each other, i.e.,
these neural unit cannot share information with each other, which is a major
drawback of ANN. Although ANN has been significantly improved in recent years
to more complex structures, such as the directed acyclic graph (DAG), these
methods also have unidirectional and acyclic bias for ANN. In this paper, we
propose a method to build a bidirectional complete graph for the nodes in the
same level of an ANN, which yokes the nodes of the same level to formulate a
neural module. We call our model as YNN in short. YNN promotes the information
transfer significantly which obviously helps in improving the performance of
the method. Our YNN can imitate neural networks much better compared with the
traditional ANN. In this paper, we analyze the existing structural bias of ANN
and propose a model YNN to efficiently eliminate such structural bias. In our
model, nodes also carry out aggregation and transformation of features, and
edges determine the flow of information. We further impose auxiliary sparsity
constraint to the distribution of connectedness, which promotes the learned
structure to focus on critical connections. Finally, based on the optimized
structure, we also design small neural module structure based on the minimum
cut technique to reduce the computational burden of the YNN model. This
learning process is compatible with the existing networks and different tasks.
The obtained quantitative experimental results reflect that the learned
connectivity is superior to the traditional NN structure.
