Detecting new edge types in a temporal network model

TL;DR

This paper introduces a novel method for detecting previously unknown edge types in temporal networks, demonstrating high accuracy especially as the network's temporal parameter increases, thus enhancing understanding of complex systems.

Contribution

The paper presents a new technique leveraging a temporal network model to effectively identify undiscovered edge types, outperforming baseline methods in accuracy.

Findings

Prediction accuracy approaches perfection as time parameter tends to infinity.

Method significantly outperforms baseline in finite-time scenarios.

Analytical and numerical results validate the effectiveness of the approach.

Abstract

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.