# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 3 Materials and Method
### Proposed Method
#### 3.3.2 View Consistency Contrastor  
Within the proposed multi-view consistency-based self-supervised representation learning framework, the view consistency contrastor plays a pivotal role in aligning latent representations across views. Its core objective is to promote semantic aggregation and latent coordination among view-specific embeddings while preserving the independence of encoders across views. As illustrated in the corresponding module diagram, the consistency contrastor constructs a \"masked hop\" neighborhood structure to simulate inter-view information propagation. Contrastive learning is then applied on the basis of this structure to ensure alignment of the latent spaces across views, as shown in Figure 2.  
Specifically, given the latent representations from \(k\) views, denoted by \(z_{d}^{1},z_{d}^{2},\ldots,z_{d}^{k}\), a set of masked neighborhoods \(\mathcal{N}_{p}\) is generated by using local perturbation and masking mechanisms to construct a multi-layer graph adjacency structure. Architecturally, a three-layer graph neural network (GNN) is employed to perform consistency learning. Each layer consists of a linear transformation followed by an activation function, with an attention-based fusion mechanism introduced to enable inter-view interactions. The channel dimensions are set as follows: the first layer uses a width of 64 and 32 channels, the second layer uses 32 and 16, and the third layer uses 16 and 8. ReLU activations are applied between layers, and dropout is used to prevent overfitting. Mathematically, the intra-view contrastive loss between two perturbed samples \(z_{i}\) and \(z_{j}\) from the same view is defined as  
\[\mathcal{L}\text{intra}=-\log\frac{\exp(\text{sim}(z_{i},z_{j})/\tau)}{\sum z _{k}\in\mathcal{B}\exp(\text{sim}(z_{i},z_{k})/\tau)}, \tag{10}\]  
where \(\text{sim}(a,b)\) denotes cosine similarity, \(\tau\) is the temperature coefficient, and \(\mathcal{B}\) represents the batch-wise set of negative samples. This loss encourages consistent representations within each view under perturbations. For inter-view latent representation pairs \(z_{d}^{i},z_{d}^{j}\), a cross-view consistency loss is defined as  
\[\mathcal{L}\text{cross}=\sum i=1^{k}\sum_{j=1,j\
eq i}^{k}\left|\phi(z_{d}^{j })-\phi(z_{d}^{j})\right|_{2}^{2} \tag{11}\]  
where \(\phi(\cdot)\) is a shared linear projector that maps all view-specific embeddings to a common latent alignment space. This loss imposes a Euclidean constraint to align the semantics across views. To enhance local contrastive stability, a graph-based masked neighborhood encoding strategy is further introduced. As shown in the module diagram, a \"masked hop\" is defined as a local \(h\)-hop subgraph centered on node \(p\), with certain edges masked and restructured. Through GNN-based propagation, positive and negative sample pairs are constructed within the masked region, enhancing the model's capacity to encode localized structural information. The associated loss function is formulated as follows:  
Figure 2: Illustration of the view-consistency contrastor module based on masked hop neighborhoods. Starting from the original AIG Netlist, multi-hop neighborhoods centered on node \(p\) are constructed within the PM netlist. \[\mathcal{L}\text{masked}=\sum p\in\mathcal{P}\left[-\log\frac{\exp(\text{sim}(z_{p}, \hat{z}p^{+}))}{\sum\hat{z}_{q}^{-}\in\mathcal{N}_{p}^{-}\exp(\text{sim}(z_{p}, \hat{z}_{q}^{-}))}\right] \tag{12}\]  
where \(\hat{z}_{p}^{+}\) denotes the reconstructed positive sample from the masked neighborhood and \(\mathcal{N}_{p}^{-}\) is the set of corresponding negative samples. This mechanism allows the model to retain semantic consistency even under information masking, which enhances the robustness of local structure modeling. Ultimately, the view consistency contrastor is integrated with the aforementioned multi-view encoder to form a complete encode-contrast-align learning loop.