Expanding the Katz Index for Link Prediction: A Case Study on a Live Fish Movement Network

TL;DR

This paper extends the Katz index to include spatial and temporal fish movement data, significantly improving disease spread prediction accuracy in aquaculture networks.

Contribution

It introduces new weighted Katz index models that incorporate spatial and temporal information, enhancing link prediction in dynamic aquaculture networks.

Findings

EWKI outperforms traditional Katz index with high precision and recall.

Combined models approach EWKI's performance but do not surpass it.

Enhanced models demonstrate the importance of spatial-temporal data in network analysis.

Abstract

In aquaculture, disease spread models often neglect the dynamic interactions between farms, hindering accuracy. This study enhances the Katz index (KI) to incorporate spatial and temporal patterns of fish movement, improving the prediction of farms susceptible to disease via live fish transfers. We modified the Katz index to create models like the Weighted Katz Index (WKI), Edge Weighted Katz Index (EWKI), and combined models (e.g., KIEWKI). These incorporate spatial distances and temporal movement patterns for a comprehensive aquaculture network connection prediction framework. Model performance was evaluated using precision, recall, F1-scores, AUPR, and AUROC. The EWKI model significantly outperformed the traditional KI and other variations. It achieved high precision (0.988), recall (0.712), F1-score (0.827), and AUPR (0.970). Combined models (KIEWKI, WKIEWKI) approached, but couldn't surpass, EWKI performance. This study highlights the value of extending Katz index models to improve disease spread predictions in aquaculture networks. The EWKI model's performance demonstrates an innovative and flexible approach to tackling spatial challenges within network analysis.