Autonomous and ubiquitous in-node learning algorithms of active directed graphs and its storage behavior
Fig 6
Example of a dynamic process for directed graph stimulus propagation.
(a) t0: Initial nodes {va,vb,vc,vd} are activated, which performs stimulus propagation according to a weighted random selection algorithm. (b) t1: {ve,vf,vg,vi,vj} becomes active after receiving stimulus from the initial nodes. (c) t2: {vh,vk} becomes active after receiving stimulus from active nodes. (d) t3: The downstream nodes {vb,vc} of vh are all occupied, so stimulus cannot be propagated. vh becomes resting again. (e) t4: After vh becomes resting state, according to the avalanche effect, vj also becomes resting state. (f) t5: The subgraph iterates to a steady state. Start to release resources, and node va releases the occupancy of ve. (g) t6: After the resources are released, the dormant node vd restarts pathfinding and successfully activates vj. (h) t7: Node vh is successfully activated after receiving the stimulus from vj and passing the stimulus to vb. The subgraph is iterated to a stable state.