Explainable Cognitive Graph Intelligence Framework for Multi-Task Graduate Employability and Salary Prediction

Accurate prediction of graduate employability and expected salary is essential for enabling timely, data-driven career interventions in academic institutions. However, most existing methods rely on standalone machine learning or deep learning models that fail to jointly capture cognitive traits, temporal academic progression, and peer-level relational structures within a unified and interpretable framework. This study introduces ExCogGNet, a hybrid multi-task learning architecture that integrates Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (BiLSTM), Transformer, and Graph Neural Network (GNN) components, optimized using Bolas Spider Optimization (BSO). The model simultaneously predicts student placement outcomes and salary levels while incorporating cognitive trait awareness. Static psychometric attributes are encoded via MLP, sequential academic trajectories are modeled using BiLSTM, contextual feature interactions are captured through Transformer-based self-attention, and relational dependencies among students are learned using Graph Attention Networks (GAT). The BSO algorithm optimizes hyperparameters, attention weights, and fusion coefficients under a unified multi-task objective. A key contribution is a counterfactual SHAP-based explainability module that converts feature attributions into actionable, personalized recommendations for improving employability and skill readiness, enabling prescriptive educational decision support. Experimental results on the Campus Recruitment dataset show 96% accuracy and 0.97 AUC for placement prediction, along with an RMSE of 24,000 INR and R² of 0.91 for salary estimation. The model outperforms baseline methods including SVM, Random Forest, XGBoost, CNN, BiLSTM, Transformer, and standalone GNNs, with statistical significance confirmed via McNemar’s test (p = 0.003), demonstrating strong predictive and interpretability performance.