# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
## 2 Methods
### ACAKG Network Topology Analysis  
As a multi-dimensional network graph of aviation accidents, the ACAKG describes various relationships between aviation accidents and accident-causing entities. To explore underlying patterns in aviation accidents, topological indicators can be utilized to conduct network topology analysis on ACAKG. Traditional topological indicators are mainly designed for one-dimensional networks. Given the unique characteristics of the ACAKG, it is essential to establish topological analysis indicators for multi-dimensional networks. These topological indicators can help uncover potential patterns in accidents from a multi-dimensional network perspective, aiding in the development of more targeted safety strategies. Building upon multi-dimensional topological analysis, this study incorporates the flight phase at the time of the accident as an analytical dimension. While reference [17] augmented the analysis with a time dimension, expanding from a one-dimensional to a multi-dimensional model, it only divided the time dimension into day and night. For aviation accidents, the flight phase dimension can more intricately reflect the correlation between the timing of accidents and their causation. This study further subdivides the time dimension based on the characteristics of the nine phases from take-off to landing. In the ACAKG model, N = {ACN, AN, TN, PN, CN}, where N denotes the set of all nodes in the topological structure, ACN represents the set of all accident causation factors, AN is the set of all accidents, TN is the set of all accident causation types, PN is the set of all accident phases, and CN is the set of all accident consequences. The strength of the causal relationships between accident causal factors and different flight phases can be measured to assess the risk level of accident causation, divided into Active Risk Level (ARL) and Passive Risk Level (PRL). The Active Risk Level indicates the likelihood that a specific accident causal factor in a certain flight phase leads to other accident causal factors, and it is calculated using Equation (8). A higher value indicates a greater likelihood of the factor to trigger other accident causal factors. Similarly, the Passive Risk Level represents the likelihood that a current accident causal factor in a specific flight phase is caused by other accident causal factors, and it is calculated using Equation (9). A higher value indicates a factor's higher susceptibility to being triggered by other accident causal factors. \[ARL_{h}^{p}=RM\times APM/\sum_{i\in HN}SPM_{hi} \tag{8}\]  
\[PRL_{h}^{p}=RM^{T}\times APM/\sum_{i\in HN}SPM_{ih} \tag{9}\]  
The distribution proportion of accident causation types describes the relationship between the current accident causation and other types of causation at different flight phases. It is divided into Active Causation Type Distribution (ACTD) and Passive Causation Type Distribution (PCTD). Active Causation Type Distribution represents the proportion distribution of other types of accident causal factors caused by the current accident causal factor at different flight phases, as shown in Equation (10). PCTD illustrates the proportion of causation types leading to the current accident causal factor within different flight phases, as shown in Equation (11). \[ACTD_{h,t}^{p}=\sum_{i\in HN}\left(CRSAM_{hi}\times CTM_{it}\times APM_{i}^{p} \right)/\left(CRSAM\times APM\right) \tag{10}\]  
\[PCTD_{t,h}^{p}=\sum_{i\in HN}\left(CRSAM_{ih}\times CTM_{it}\times APM_{i}^{p }\right)/\left(CRSAM^{T}\times APM\right) \tag{11}\]  
The accident causation correlation types are divided into Direct Correlation Types (DCTs) and Indirect Correlation Types (ICTs), indicating the strength of direct or indirect causal relationships between accident causation types at different flight phases, and they are calculated using Equations (12) and (13), respectively.