University Innovation and Entrepreneurship Ecological Network Modeling Based on GNN
Main Article Content
Abstract
University innovation and entrepreneurship ecological networks exhibit significant heterogeneity, dynamic evolution, and complex semantic dependencies, making accurate representation learning and temporal modeling particularly challenging. Similar heterogeneous spatio-temporal information fusion problems are increasingly encountered in intelligent electromagnetic sensing, communication networks, and advanced signal processing systems, highlighting the importance of robust graph-based analytical frameworks. To address these issues, this paper proposes a multi-task modeling framework for temporally heterogeneous graphs based on a Relational Graph Attention Network (R-GAT) and a Gated Recurrent Unit (GRU). The R-GAT module distinguishes heterogeneous semantic relationships through a relation-aware attention mechanism, while the time-decaying GRU effectively captures historical behaviors with irregular temporal intervals. Interpretable attention pooling is further introduced to generate graph-level representations, and a multi-task output head with uncertainty-adaptive weighting jointly optimizes classification, regression, and contrastive self-supervised objectives for comprehensive ecosystem modeling. Experimental results demonstrate that the proposed method achieves an accuracy of 0.96 ± 0.01 and an F1 score of 0.87 ± 0.02 for entrepreneurial potential classification, together with an MAE of 0.02 ± 0.01 and an RMSE of 0.04 ± 0.01 for ecosystem health regression, consistently outperforming conventional baselines. The proposed framework enhances both predictive accuracy and interpretability while providing an effective heterogeneous graph modeling strategy that may offer methodological insights for spatio-temporal information processing and intelligent electromagnetic signal analysis in advanced engineering applications.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Q. Wang, Y. Li, Y. Yang, et al., “University-led entrepreneurship ecosystem building in underserved communities: From a network perspective,” Geographical Review, vol. 114, no. 3, pp. 353-377, 2024, doi: 10.1080/00167428.2023.2256000.
A. Diaz-Gonzalez and N. A. Dentchev, “A resource-based view on the role of universities in supportive ecosystems for social entrepreneurs,” Business and Society Review, vol. 127, no. 3, pp. 537-590, 2022, doi: 10.1111/basr.12281.
M. Leal, C. Leal, and R. Silva, “The involvement of universities, incubators, municipalities, and business associations in fostering entrepreneurial ecosystems and promoting local growth,” Administrative Sciences, vol. 13, no. 12, pp. 245-267, 2023, doi: 10.3390/admsci13120245.
Y. Zhang, J. Liu, and Y. Xie, “The construction of innovation and entrepreneurship education ecosystem in universities based on social network analysis,” International Journal of Networking and Virtual Organisations, vol. 32, no. 1, pp. 135-157, 2025, doi: 10.1504/IJNVO.2025.145374.
A. Malik, E. M. Onyema, S. Dalal, et al., “Forecasting students adaptability in online entrepreneurship education using modified ensemble machine learning model,” Array, vol. 19, no. 1, pp. 1-11, 2023, doi: 10.1016/j.array.2023.100303.
D. Hao, “Hierarchical Analysis and Graph Attention Network for University Students Innovation and Entrepreneurship Learning Platform,” Informatica, vol. 48, no. 19, pp. 145-158, 2024, doi: 10.31449/inf.v48i19.6545.
A. Subakti, H. Murfi, and N. Hariadi, “The performance of BERT as data representation of text clustering,” Journal of big Data, vol. 9, no. 1, pp. 15-35, 2022, doi: 10.1186/s40537-022-00564-9.
E. C. Garrido-Merchan, R. Gozalo-Brizuela, and S. Gonzalez-Carvajal, “Comparing BERT against traditional machine learning models in text classification,” Journal of Computational and Cognitive Engineering, vol. 2, no. 4, pp. 352-356, 2023, doi: 10.47852/bonviewJCCE3202838.
R. Patil, S. Boit, V. Gudivada, and J. Nandigam, “A survey of text representation and embedding techniques in nlp,” IEEE Access, vol. 11, no. 1, pp. 36120-36146, 2023, doi: 10.1109/ACCESS.2023.3266377.
O. R. Olaniran and A. R. R. Alzahrani, “Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study,” Mathematics, vol. 13, no. 6, pp. 956-988, 2025, doi: 10.3390/math13060956.
W. Suo, B. A. Muhammad, Z. Zhang, and B. Liu, “GAT-LS: A Graph Attention Network for Learning Style Detection in Online Learning Environment,” Journal of Networking and Network Applications, vol. 4, no. 2, pp. 60-72, 2024, doi: 10.33969/J-NaNA.2024.040202.
L. Li and Z. Wang, “Calibrated q-matrix-enhanced deep knowledge tracing with relational attention mechanism,” Applied Sciences, vol. 13, no. 4, pp. 2541-2564, 2023, doi: 10.3390/app13042541.
Y. Fang, X. Li, R. Ye, et al., “Relation-aware graph convolutional networks for multi-relational network alignment,” ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 2, pp. 1-23, 2023, doi: 10.1145/3579827.
L. Yu, L. Sun, B. Du, et al., “Heterogeneous graph representation learning with relation awareness,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 6, pp. 5935-5947, 2022, doi: 10.1109/TKDE.2022.3160208.
M. Niu, “Quality evaluation of innovation and entrepreneurship education based on modelling of students behavior sequences,” International Journal of Emerging Technologies in Learning (Online), vol. 18, no. 9, pp. 17-31, 2023, doi: 10.3991/ijet.v18i09.40227.
T. Kavipriya and M. N. Kumar, “Predicting Student Placement Outcomes with Bi-GRU: A Deep Learning Approach,” Cuestiones de Fisioterapia, vol. 54, no. 3, pp. 1457-1470, 2025, doi: 10.48047/9z4wjs67.
L. Tao, “Improving the Quality of Vocational Education in Higher Vocational Colleges Based on Deep Learning Technology: Student Learning Prediction and Personalized Recommendation,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 29, no. 2, pp. 407-416, 2025, doi: 10.20965/jaciii.2025.p0407.
D. Cai, S. Qian, Q. Fang, et al., “User cold-start recommendation via inductive heterogeneous graph neural network,” ACM Transactions on Information Systems, vol. 41, no. 3, pp. 1-27, 2023, doi: 10.1145/3560487.
Q. Hu, L. Tan, D. Gong, et al., “Graph attention networks with adaptive neighbor graph aggregation for cold-start recommendation,” Journal of Intelligent Information Systems, vol. 63, no. 1, pp. 293-312, 2025, doi: 10.1007/s10844-024-00888-3.
P. Zhou, X. Xie, Z. Lin, and S. Yan, “Towards understanding convergence and generalization of AdamW,” IEEE transactions on pattern analysis and machine intelligence, vol. 46, no. 9, pp. 6486-6493, 2024, doi: 10.1109/TPAMI.2024.3382294.
M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Computing and Applications, vol. 35, no. 23, pp. 17095-17112, 2023, doi: 10.1007/s00521-023-08568-z.
O. V. Johnson, C. Xinying, K. W. Khaw, and M. H. Lee, “ps-CALR: periodic-shift cosine annealing learning rate for deep neural networks,” IEEE Access, vol. 11, no. 1, pp. 139171-139186, 2023, doi: 10.1109/ACCESS.2023.3340719.
C. Zhang, Y. Shao, H. Sun, et al., “The WuC-Adam algorithm based on joint improvement of Warmup and cosine annealing algorithms,” Math. Biosci. Eng, vol. 21, no. 1, pp. 1270-1285, 2024, doi: 10.3934/mbe.2024054.