Student Group Division Algorithm based on Multi-view Attribute Heterogeneous Information Network |
Jia, Xibin
(Faculty of Information Technology, Beijing University of Technology)
Lu, Zijia (Faculty of Information Technology, Beijing University of Technology) Mi, Qing (Faculty of Information Technology, Beijing University of Technology) An, Zhefeng (Faculty of Humanities and Social Science, Beijing University of Technology) Li, Xiaoyong (Information Technology Support Center, Beijing University of Technology) Hong, Min (Department of Computer Software Engineering, Soonchunhyang University) |
1 | Q. Hu, H. Rangwala, "Academic Performance Estimation with Attention-based Graph Convolutional Networks," in Proc. of the 12th International Educational Data Mining Society, Montreal, Canada, pp. 69-78, 2019. |
2 | H. Li, H. Wei, Y. Wang, Y. Song, H. Qu, "Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network," in Proc. of the 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, pp. 2589-2596, 2020. |
3 | J.-L. Hung, M.-C. Wang, S. Wang, M. Abdelrasoul, Y. Li, H. Wu, "Identifying At-Risk Students for Early Interventions-A Time-Series Clustering Approach," IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 1, pp. 45-55, 2017. DOI |
4 | D. Bo, X. Wang, C. Shi, M. Zhu, E. Lu, P. Cui, "Structural Deep Clustering Network," in Proc. of the Web Conference 2020, Taipei, Taiwan, China, pp. 1400-1410, 2020. |
5 | M. Xie, H. Yin, H. Wang, F. Xu, W. Chen, S. Wang, "Learning Graph-based POI Embedding for Location-based Recommendation," in Proc. of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, Indiana, USA, pp.15-24, 2016. |
6 | Y. Sun, J. Han, "Mining heterogeneous information networks: a structural analysis approach," ACM SIGKDD Explorations Newsletter, vol. 14, no. 2, pp. 20-28, 2013. DOI |
7 | J. Gong, S. Wang, J. Wang, et al, "Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View," in Proc. of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China, pp. 79-88, 2020. |
8 | M. -E.-J. Newman, M. Girvan, "Finding and evaluating community structure in networks," Physical Review E, vol. 69, no. 2, pp. 026113, 2004. DOI |
9 | Y. Zhang, Y. Xiong, X. Kong, Z. Niu, Y. Zhu, "IGE+: A Framework for Learning Node Embeddings in Interaction Graphs," IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 3, pp. 1032-1044, 2021. |
10 | S. Fan, X. Wang, C. Shi, E. Lu, K. Lin, B. Wang, "One2Multi Graph Autoencoder for Multi-view Graph Clustering," in Proc. of the Web Conference 2020, Taipei, Taiwan, China, pp. 3070-3076, 2020. |
11 | T.-N. Kipf, M. Welling, "Variational graph auto-encoders," arXiv preprint arXiv: 1611.07308, 2016. |
12 | S. Li Shen, Z. Zhao, R. Hu, W. Li, T. Liu, X. Du, "Analogical Reasoning on Chinese Morphological and Semantic Relations," in Proc. of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Melbourne, Australia, pp. 138-143, 2018. |
13 | X. Zhang, H. Zheng, X. Li, S. Du, H. Zhu, "You are where you have been: Sybil detection via geo-location analysis in OSNs," in Proc. of 2014 IEEE Global Communications Conference, Austin, TX, USA, pp. 698-703, 2014. |
14 | A. Moubayed, M. Injadat, A. Shami, H. Lutfiyya, "Relationship Between Student Engagement and Performance in E-Learning Environment Using Association Rules," in Proc. of 2018 IEEE World Engineering Education Conference, Buenos Aires, Argentina, pp. 1-6, 2018. |
15 | M. McCord, M. Chuah, "Spam detection on twitter using traditional classifiers," in Proc. of the 8th International Conference on Autonomic and Trusted Computing, Springer, Berlin, Heidelberg, pp. 175-186, 2011. |
16 | D. Bunic, I. Jugo, B. Kovacic, "Analysis of clustering algorithms for group discovery in a webbased intelligent tutoring system," in Proc. of 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia pp. 759-765, 2019. |
17 | X. Kong, P.S. Yu, Y. Ding, D.-J. Wild, "Meta path-based collective classification in heterogeneous information networks," in Proc. of the 21st ACM International Conference on Information and Knowledge Management, Maui, Hawaii, USA, pp. 1567-1571, 2012. |
18 | Y. Dong, N.-V. Chawla, A. Swami, "Metapath2vec: Scalable Representation Learning for Heterogeneous Networks," in Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, pp. 135-144, 2017. |
19 | S. Fan, C. Shi, L. Hu, B. Ma, Y. Li, "Metapath-guided heterogeneous graph neural network for intent recommendation," in Proc. of 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA, pp. 2478-2486, 2019. |
20 | Q. Le, T. Mikolov, "Distributed representations of sentences and documents," in Proc. of the 31st International Conference on Machine Learning, Beijing, China, pp.1188-1196, 2014. |
21 | B. Perozzi, R.-A. Rfou, S. Skena, "Deepwalk: Online learning of social representations," in Proc. of 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 701-710, 2014. |
22 | H. Yao, M. Nie, H. Su, H. Xia, D. Lian, "Predicting academic performance via semi-supervised learning with constructed campus social network," in Proc. of International Conference on Database Systems for Advanced Applications, Springer, Cham, pp. 597-609, 2017. |
23 | M. Zhou, D. Yang, "Research progress on educational data mining: A survey," Journal of Software, vol. 26, no. 11, pp. 3026-3042, 2015. |
24 | C. David, B. Cecile, P. Francois, "Integrating heterogeneous information within a social network for detecting communities," in Proc. of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), Niagara Falls, ON, Canada, pp.1453- 1454, 2013. |
25 | C. Shi, Y. Li, J. Zhang, Y. Sun, P. Yu, "A Survey of Heterogeneous Information Network Analysis," IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 1, pp. 17-37, 2015. DOI |
26 | C. Shi, B. Hu, W.-X. Zhao, P. Yu, "Heterogeneous information network embedding for recommendation," IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 357-370, 2019. DOI |
27 | T. Sona, B. Asgarali, "Community detection in social networks using affinity propagation with adaptive similarity matrix," Big Data, vol. 8, no. 3, pp. 189-202, 2020. DOI |
28 | K. Thomas, W. Max, "Semi-supervised classification with graph convolutional networks," arXiv preprint arXiv: 1609.02907, 2016. |
29 | A. Sankar, Y. Liu, J. Yu, N. Shah, "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms," in Proc. of the Web Conference 2021, Ljubljana, Slovenia, pp. 2535-2546, 2021. |
30 | B. Sekeroglu, K. Dimililer, K. Tuncal, "Student Performance Prediction and Classification Using Machine Learning Algorithms," in Proc. of the 2019 8th International Conference on Educational and Information Technology, Cambridge, United Kingdom, pp. 7-11, 2019. |
31 | Z. Wang, H. Liu, Y. Du, Z. Wu, X. Zhang, "Unified embedding model over heterogeneous information network for personalized recommendation," in Proc. of the 28th International Joint Conference on Artificial Intelligence, Macao, China, pp. 3813-3819, 2019. |
32 | C. Wang, S. Pan, R. Hu, G. Long, J. Jiang, C. Zhang, "Attributed Graph Clustering: A Deep Attentional Embedding Approach," in Proc. of the 28th International Joint Conference on Artificial Intelligence, Macao, China, pp. 3670-3676, 2019. |
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