| 905 | -- | 948 | Muhammad Irfan Yousuf, Suhyun Kim. Guided sampling for large graphs |
| 949 | -- | 979 | Yan Zhu 0014, Shaghayegh Gharghabi, Diego Furtado Silva, Hoang Anh Dau, Chin-Chia Michael Yeh, Nader Shakibay Senobari, Abdulaziz Almaslukh, Kaveh Kamgar, Zachary Zimmerman, Gareth J. Funning, Abdullah Mueen, Eamonn J. Keogh. The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code |
| 980 | -- | 1021 | Jinghan Meng, Napath Pitaksirianan, Yi-Cheng Tu. Counting frequent patterns in large labeled graphs: a hypergraph-based approach |
| 1022 | -- | 1071 | Michele Linardi, Yan Zhu 0014, Themis Palpanas, Eamonn J. Keogh. Matrix profile goes MAD: variable-length motif and discord discovery in data series |
| 1072 | -- | 1103 | Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy. struc2gauss: Structural role preserving network embedding via Gaussian embedding |
| 1104 | -- | 1135 | Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh. An ultra-fast time series distance measure to allow data mining in more complex real-world deployments |
| 1136 | -- | 1174 | Saeid Hosseini, Saeed Najafi Pour, Ngai-Man Cheung, Hongzhi Yin, Mohammad Reza Kangavari 0001, Xiaofang Zhou 0001. TEAGS: time-aware text embedding approach to generate subgraphs |
| 1175 | -- | 1200 | Steven Elsworth, Stefan Güttel. ABBA: adaptive Brownian bridge-based symbolic aggregation of time series |
| 1201 | -- | 1234 | Leonardo Pellegrina, Fabio Vandin. Efficient mining of the most significant patterns with permutation testing |