Communication between gray matter regions underpins all facets of brain function. We study inter-areal communication in the human brain using intracranial EEG recordings, acquired following 29,055 single-pulse direct electrical stimulations in a total of 550 individuals across 20 medical centers (average of 87 ± 37 electrode contacts per subject). We found that network communication models—computed on structural connectivity inferred from diffusion MRI—can explain the causal propagation of focal stimuli, measured at millisecond timescales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional, and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (R2=46% in data from held-out medical centers). Our work contributes toward the biological validation of concepts in network neuroscience and provides insight into how connectome topology shapes polysynaptic inter-areal signaling. We anticipate that our findings will have implications for research on neural communication and the design of brain stimulation paradigms. •We analyze the propagation of focal, direct electrical stimulation in the human brain•Network communication models explain electrical pulse transmission across the cortex•Measures of network diffusion more explanatory than transmission via shortest paths•Machine-learning model predicts the effects of brain stimulation in held-out samples Seguin et al. use a large dataset of direct electrical stimulation to empirically study the propagation of electrical pulses across the entire human brain at high-spatiotemporal resolution. The authors show that models of network communication computed on the human connectome, inferred non-invasively from MRI, can explain causal, millisecond-resolution and cortex-wide neural signal transmission.