# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 3 Proposed Approach for Emotion Recognition
### Classification Using CA-WGNN  
The Cumulative Attribute-Weighted Graph Neural Network (CA-WGNN) model, which has been used in geometric deep learning, uses a weighted graph structure to map interrelationships between modalities. This groundbreaking approach not only fosters a comprehensive understanding of emotions by tapping into the synergistic potential of diverse data sources but also strives to classify emotions holistically. In the realm of emotion classification, it is paramount to adopt a holistic approach that integrates insights from multiple modalities. This method is especially effective in capturing the nuanced spectrum of human emotions. Specifically, the classification model distinguishes between three primary categories of emotions:  
1. **Positive Emotions:** These include feelings such as joy, excitement, and a general sense of positivity. These emotions are characterized by their uplifting and affirmative nature. 2. **Negative Emotions:** This category encompasses emotions like sadness, fear, and disgust, which are typically associated with adverse experiences or perceptions. 3. **Neutral Emotion:** Representing a state that is neither explicitly positive nor negative, this category captures the subtlety of emotions that do not fall distinctly into the other two classifications. The sophisticated structure of CA-WGNN, which is connectivity-driven, excels in learning from input graphs and deciphering complex system interactions through iterative data analysis across connections. This enables the model to generalize effectively to unfamiliar graphs, making predictions at both the network and node levels. Thanks to its advanced capabilities, CA-WGNN is instrumental in a variety of applications, showcasing its versatility and effectiveness. In terms of implementation, the pseudo-code for CA-WGNN inference, as illustrated in Algorithm 1, delineates the step-by-step process of this model's operation. Moreover, the model's detailed structure, as shown in Figure 6, further elucidates its functional dynamics and connectivity-driven approach, highlighting its role as a pivotal tool in the realm of geometric deep learning. Figure 6: Structure of CA-WGNN. ```
1:procedure GNN Operator
2:\(F\leftarrow\) Number of layers in the GNN
3:\(C\leftarrow\) Set of nodes in graph \(S\) (assumed static)
4:\(A\leftarrow\) Set of edges in graph \(S\) (assumed static)
5:Initialize Nodes and Edges:
6:for \(c\in C\) do
7:\(z^{0}_{c}\leftarrow[y_{c},0,\dots,0]\)
8:for \(a\in A\) do
9:\(s^{0}_{a}\leftarrow[h_{c},0,\dots,0]\)
10:GNN Layered processing:
11:for \(f=1\) to \(F\) do
12:Edge processing:(Order of edge and node processing may be interchanged or even interspersed.) 13:for \(a\in A\) do (Order of aggregation and combination may be interchanged if aggregation is linear.) 14:\(p^{(f)}_{a}=\rho^{(f)}_{A}(s^{(f-1)}_{a},z^{(f-1)}_{w}:w\in M(a))\)
15:\(s^{(f)}_{a}=\phi^{(f)}_{A}(p^{(f)}_{a})\),
16:Node processing:(Order of edge and node processing may be interchanged or even interspersed.) 17:for \(c\in C\) do (The order of aggregation and combination may be interchanged if aggregation is linear.) 18:\(e^{(f)}_{c}=\rho^{(f)}_{C}(z^{(f-1)}_{c},z^{(f-1)}_{w}:w\in M(c))\)
19:\(z^{(f)}_{c}=\phi^{(f)}_{C}(e^{(f)}_{c})\),
20:Readout:
21:\(\hat{x}=\phi_{S}(\rho S(\{z^{F}_{c},s^{F}_{a}:c,a\in S\}))\). 22:end procedure. ```  
**Algorithm 1** Algorithm of CA-WGNN for emotion recoginition  
The CA-WGNN model starts with the graph's edges, vertices, and feature vectors as inputs (step 9). It executes in layers, with each layer updating the feature vectors of all connected vertices and edges concurrently (steps 11, 14, 15). Combination functions, which can be represented by neural networks, are used to update the combined edges and vertices (steps 13, 17). The readout process is carried out by an appropriate function, often a neural network (step 18).