  Networks representing complex systems in nature and society usually involve
multiple interaction types. These types suggest essential information on the
interactions between components, but not all of the existing types are usually
discovered. Therefore, detecting the undiscovered edge types is crucial for
deepening our understanding of the network structure. Although previous studies
have discussed the edge label detection problem, we still lack effective
methods for uncovering previously-undetected edge types. Here, we develop an
effective technique to detect undiscovered new edge types in networks by
leveraging a novel temporal network model. Both analytical and numerical
results show that the prediction accuracy of our method is perfect when the
model networks' time parameter approaches infinity. Furthermore, we find that
when time is finite, our method is still significantly more accurate than the
baseline.

Physics and Society;Data Analysis, Statistics and Probability