> Recall that in our general definition a feed-forward neural network is a computational graph whose nodes are computing units and whose directed edges transmit numerical information from node to node.
> Each computing unit is capable of evaluating a single primitive function of its input. In fact the network represents a chain of function compositions which transfor m an input to an output vector (called a pattern).
From: https://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf (1996)
Another ancestor would be the Data-Flow paradigm:
> .. programming paradigm that internally represents applications as a directed graph, similarly to a dataflow diagram. Applications are represented as a set of nodes (also called blocks) with input and/or output ports in them. These nodes can either be sources, sinks or processing blocks to the information flowing in the system. Nodes are connected by directed edges that define the flow of information between them.
From: https://paginas.fe.up.pt/~prodei/dsie12/papers/paper_17.pdf (2012)