## 2 The Proposed Methodology
### The Construction of Slope Stability Evaluation Model  
#### 2.2.1 Graph Representation Learning-Node Vectorization  
Graph embedding is a process of mapping graph data (usually to construct a high-dimensional dense matrix) into low-dimensional dense vectors in order to solve the difficulty in efficiently inputting graph data into neural networks [38; 39]. Graph embeddings were used to transform the attribute graph of a knowledge graph into a vector or a set of vectors. Embeddings were also used to capture the topology of the graphs, the relationships between vertices, and other related information about the graph, subgraphs, and vertices. As we all know, graphs contain abundant structural information, which essentially corresponds to a nonlinear structure generated by the internal correlation of various influencing factors. The slope unit was represented as a node in the graph, referred to as the top node. The goal was to use the surrounding nodes to encode the top-level nodes and to store the structural information around each node in this virtual network by training the virtual network. The vectors output by this network after the surrounding nodes were inputted were exactly the embedded vectors of these top nodes. This node vectorization method could also vectorize the attributes of nodes and edges in the attribute graph (including numerical and enumeration vectorization). Ultimately, it was expected that the vectorized feature vectors would carry the topology and attribute information of subordinate nodes, thus facilitating the classification of neural network input and training. Graph embedding models can be categorized based on their algorithmic principles into matrix factorization-based methods, random walk-based methods, graph neural network-based methods, and hybrid approaches. The core idea of random walk-based graph embedding methods is to analogize node sequences in graphs to word sequences in natural language processing (NLP), leveraging NLP techniques to learn node representations. DeepWalk [40], a representative model in this category, integrates random walks with the Word2 Vec algorithm [41] through two main steps: generating node sequences via random walks and learning vector representations using a Skip-Gram model. Specifically, it first generates numerous node sequences through random walks, starting from each node and simulating traversal paths on the graph to capture neighborhood relationships. These sequences then train a Skip-Gram model that predicts contextual nodes around targets, enabling low-dimensional vector representations where similar nodes are positioned closer in the vector space. Node2 Vec [42], an extension of DeepWalk, enhances flexibility in capturing homophily and structural equivalence by incorporating breadth-first search (BFS) and depth-first search (DFS) strategies into random walk sequence generation. Two critical parameters--return parameter p and in-out parameter q--regulate the walk strategy. The return parameter controls the probability of revisiting previous nodes, while the in-out parameter adjusts the transitions between unvisited nodes. Tuning these parameters allows Node2 Vec to balance local and global structural features: higher \(p\) values prioritize local neighborhood exploration, whereas larger q values promote jumps to distant nodes, thereby encoding both micro- and macro-structural information. This adaptive design enables more nuanced graph structure characterization compared to baseline methods. Therefore, the Node2 Vec method was adopted to generate feature vectors for each ontology instance (represented as yellow rectangles) based on the structure of the constructed knowledge graph. As illustrated in Figure 2, the orange octagonal node was used to denote a slope unit, while the blue nodes represented influencing entities such as the properties of the rock and soil mass, including stratigraphic lithology, physical characteristics of rocks,degree of weathering, and structural features.