Our recent work of attributed network embedding, knowledge graph alignment, and influential nodes tracking in a dynamic network.

  1.  Shichao Pei, Lu Yu, Guoxian Yu and Xiangliang Zhang. REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs. Accepted by the 26th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), August 22 - 27, 2020, San Diego, CA, USA.  (Acceptance rate 216/1279=16.9%).​

  2. Zaiqiao Meng, Shangsong Liang, Xiangliang Zhang, Richard Mccreadie, Idah Ounis. Jointly Learning Representations of Nodes and Attributes for Attributed Networks. ACM Transactions on Information Systems (TOIS), 38(2), 1–32. 2020.

  3. Peng Jia, Pinghui Wang, Yuchao Zhang, Xiangliang Zhang, Jing Tao, Jianwei Ding, Xiaohong Guan, Don Towsley. Accurately Estimating User Cardinalities and Detecting Super Spreaders over Time. To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020​.
  4. Uchenna​ Akujuobi, Qiannan Zhang, Han Yufei, Xiangliang Zhang. Recurrent Attention Walk for Semi-supervised Classification. In the proceedings of The 13th ACM International WSDM Conference​. Feb 3-7, 2020, Houston, Texas. (Acceptance rate =91/615 = 15%)​ (paper at arXiv)
  5. ​​Yujun Chen, Ke Sun, Juhua Pu, Zhang Xiong, Xiangliang Zhang. GraPASA: Parametric Graph Embedding via Siamese Architecture. Accepted by Information Sciences 512: 1442-1457 (2020).​​​
  6. Yujun Chen, Juhua Pu, Xingwu Liu, Xiangliang Zhang. Gaussian Mixture Embedding of Multiple Node Roles in Networks.  World Wide Web 23(2): 927-950 (2020) [PDF​]
  7. Uchenna Akujuobi, Han Yufei, Qiannan Zhang, Xiangliang Zhang. Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification. In the proceedings of 19th IEEE International Conference on Data Mining (ICDM 2019), November 8-11, 2019, Beijing, China (Regular paper, Acceptance  rate= 95/1046 = 9.08%).​ (paper at arXiv​)
  8. Basmah Altaf, Uchenna Akujuobi, Lu Yu, Xiangliang Zhang, Dataset Recommendation via Variational Graph Autoencoder. In the proceedings of 19th IEEE International Conference on Data Mining (ICDM 2019), November 8-11, 2019, Beijing, China (Regular paper, Acceptance rate= 95/1046 = 9.08%).​
  9. Yujun Chen, Yuanhong Wang, Yutao Zhang, Juhua Pu,  Xiangliang Zhang. AMENDER: an Attentive and Aggregate Multi-layered Network for Dataset Recommendation. In the proceedings of 19th IEEE International Conference on Data Mining (ICDM 2019), November 8-11, 2019, Beijing, China (Short paper, Acceptance rate= 18.5%).​
  10. Shichao Pei, Lu Yu, Xiangliang Zhang. Improving Cross-lingual Entity Alignment via Optimal Transport. In the proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)​, August 10-16, 2019, Macao, China (Acceptance rate=850/4752=17.9%) [PDF][Bib][Slides].
  11. Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang. ActiveHNE: Active Heterogeneous Network Embedding. In the proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)​​,  August 10-16, 2019, Macao, China. (Acceptance rate=850/4752=17.9%).
  12. Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang. Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference.  In the proceedings of by TheWebConf 2019 (previously known as WWW conference),  May 13-17, San Francisco, CA, USA (short paper, acceptance rate ~ 20%) [PDF][Bib][Slides​][Code​].
  13. Guolei Sun, Xiangliang Zhang. A Novel Framework for Node/Edge Attributed Graph Embedding. In the proceedings of the 23rd  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), 14-17 April 2019, Macau SAR, China (regular paper, acceptance rate = 140/567 =24.7%).
  14. Junzhou Zhao, Shuo Shang, Pinghui Wang, John C.S. Lui, and Xiangliang Zhang. Submodular Optimization Over Streams with Inhomogeneous Decays. In the proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), January 27 – February 1, Honolulu, Hawaii, USA. (Acceptance rate = 1150/7095 = 16.2%) [PDF​​][Bib][Slides​].​
  15. Zaiqiao Meng, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. Co-Embedding Attributed Networks. In the proceedings of The Twelfth International Conference on Web Search and Data Mining (WSDM 2019), February ​11-15, 2019, Melbourne, Australia  (Acceptance rate = 84/511 = 16%) [PDF​​][Bib][Slides​][Code​].
  16. Junzhou Zhao, Shuo Shang, Pinghui Wang, John C.S. Lui, and Xiangliang Zhang: Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks. In the proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019)​, 8-12 April 2019, Macau SAR, China [PDF​​][Bib][Slides​].
  17. Pinghui Wang, Peng Jia, Xiangliang Zhang, Jing Tao, Xiaohong Guan, and Don Towsley: Utilizing Dynamic Properties of Sharing Bits and Registers to Estimate User Cardinalities over Time. In the proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019)​, 8-12 April 2019, Macau SAR, China.
  18. Uchenna Akujuobi, Ke Sun, and Xiangliang Zhang:  Mining top-k Popular Datasets via a Deep Generative Model. In the proceedings of  by IEEE Big Data 2018, Seattle, WA, USA, December 10-13, 2018 (regular paper, acceptance rate = 98/518 =18.9%)[PDF​][Bib][Slides​].
  19. Shangsong Liang, Xiangliang Zhang, Zhaochun Ren and Evangelos Kanoulas: Dynamic Embeddings for User Profiling in Twitter. In Proceedings of the 24th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018), pp. 1764-1773, London, UK, August 19 - 23, 2018 (long presentation, acceptance rate= 107/ 983 =10.9%)(overall acceptance rate=181/983=18.4%)[PDF​​][Bib][Slides​]​​.
  20. Pinghui Wang, Yiyan Qi, Yu Sun, Xiangliang Zhang, Jing Tao, Xiaohong Guan. Approximately Counting Triangles in Large Graph Streams Including Edge Duplicates with a Fixed Memory Usage. In Proceedings of  Very Large Data Bases (VLDB 2017), 11(2): 162-175, 2017 [PDF][Bib].​
  21. Pinghui Wang, Junzhou Zhao, Xiangliang Zhang, Zhenguo Li, Jiefeng Cheng, John C.S. Lui, Don Towsley, Jing Tao, Xiaohong Guan. MOSS-5: A Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs. In IEEE Transactions on Knowledge and Data Engineering (TKDE)​, 30(1): 73-86, 2017.​