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2. HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning

Author

Cai, Xinjun, Shang, Jiaxing, Hao, Fei, Liu, Dajiang, and Zheng, Linjiang

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Social and Information Networks, 68T07, I.2.6
Abstract

Many real-world data can be represented as heterogeneous graphs with different types of nodes and connections. Heterogeneous graph neural network model aims to embed nodes or subgraphs into low-dimensional vector space for various downstream tasks such as node classification, link prediction, etc. Although several models were proposed recently, they either only aggregate information from the same type of neighbors, or just indiscriminately treat homogeneous and heterogeneous neighbors in the same way. Based on these observations, we propose a new heterogeneous graph neural network model named HMSG to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors. Specifically, we first decompose the heterogeneous graph into multiple metapath-based homogeneous and heterogeneous subgraphs, and each subgraph associates specific semantic and structural information. Then message aggregation methods are applied to each subgraph independently, so that information can be learned in a more targeted and efficient manner. Through a type-specific attribute transformation, node attributes can also be transferred among different types of nodes. Finally, we fuse information from subgraphs together to get the complete representation. Extensive experiments on several datasets for node classification, node clustering and link prediction tasks show that HMSG achieves the best performance in all evaluation metrics than state-of-the-art baselines., Comment: 12 pages, 3 figures, 6 tables

Published
2021

3. A Joint Framework for Knowledge Extraction from Flight Training Comments

Author

Zhang, Yuxuan, Shang, Jiaxing, Zheng, Linjiang, Wu, Quanwang, Cao, Weiwei, Sun, Hong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Guo, Yinzhang, editor, Song, Xiaoxia, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Gao, Liping, editor, and Du, Bowen, editor

Published
2023
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9. EBA: An Adaptive Large Neighborhood Search-Based Approach for Edge Bandwidth Allocation

Author

Hu, Qinghong, Peng, Qinglan, Shang, Jiaxing, Li, Yong, He, Junjie, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Gao, Honghao, editor, Wang, Xinheng, editor, Wei, Wei, editor, and Dagiuklas, Tasos, editor

Published
2022
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10. A Graph Neural Network-Based Approach for Predicting Second Rise of Information Diffusion on Social Networks

Author

Shang, Jiaxing, Wang, Yijie, Gong, Yuxin, Zou, Yanli, Cai, Xinjun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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11. ConCas: Cascade Popularity Prediction Based on Topic-Aware Graph Contrastive Learning

Author

Ling, Chen, Zhang, Xianren, Shang, Jiaxing, Liu, Dajiang, Li, Yong, Xie, Wu, Qiang, Baohua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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12. CollaborateCas: Popularity Prediction of Information Cascades Based on Collaborative Graph Attention Networks

Author

Zhang, Xianren, Shang, Jiaxing, Jia, Xueqi, Liu, Dajiang, Hao, Fei, Zhang, Zhiqing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhattacharya, Arnab, editor, Lee Mong Li, Janice, editor, Agrawal, Divyakant, editor, Reddy, P. Krishna, editor, Mohania, Mukesh, editor, Mondal, Anirban, editor, Goyal, Vikram, editor, and Uday Kiran, Rage, editor

Published
2022
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15. A Deep Sequence-to-Sequence Method for Aircraft Landing Speed Prediction Based on QAR Data

Author

Kang, Zongwei, Shang, Jiaxing, Feng, Yong, Zheng, Linjiang, Liu, Dajiang, Qiang, Baohua, Wei, Ran, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, Zhisheng, editor, Beek, Wouter, editor, Wang, Hua, editor, Zhou, Rui, editor, and Zhang, Yanchun, editor

Published
2020
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16. RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis

Author

Jin, Ziwei, Shang, Jiaxing, Zhu, Qianwen, Ling, Chen, Xie, Wu, Qiang, Baohua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, Zhisheng, editor, Beek, Wouter, editor, Wang, Hua, editor, Zhou, Rui, editor, and Zhang, Yanchun, editor

Published
2020
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21. A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

Author

Shang, Jiaxing, Liu, Lianchen, Xie, Feng, Chen, Zhen, Miao, Jiajia, Fang, Xuelin, and Wu, Cheng

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society, H.2.8
Abstract

In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a fine-grained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the algorithm of Blondel et al for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity., Comment: 9 pages, 6 figures, 3 tables, 6th SNA-KDD Workshop (2012)

Published
2014

22. ExtTra: Short-Term Traffic Flow Prediction Based on Extremely Randomized Trees

Author

Shang, Jiaxing, Yan, Xiaofan, Feng, Linhui, Dong, Zheng, Wang, Haojie, Zhou, Shangbo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Cheng, Long, editor, Leung, Andrew Chi Sing, editor, and Ozawa, Seiichi, editor

Published
2018
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27. A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks

Author

Wu, Hongchun, Shang, Jiaxing, Zhou, Shangbo, Feng, Yong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
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28. App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach

Author

Shang, Jiaxing, Wang, Jinghao, Liu, Ge, Wu, Hongchun, Zhou, Shangbo, Feng, Yong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
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29. Effective Influence Maximization Based on the Combination of Multiple Selectors

Author

Shang, Jiaxing, Wu, Hongchun, Zhou, Shangbo, Liu, Lianchen, Tang, Hongbin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ma, Liran, editor, Khreishah, Abdallah, editor, Zhang, Yan, editor, and Yan, Mingyuan, editor

Published
2017
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34. IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional Networks

Author

Li, Xu, Shang, Jiaxing, Zheng, Linjiang, Wang, Qixing, Liu, Dajiang, Liu, Xiaodong, Li, Fan, Cao, Weiwei, and Sun, Hong

Abstract

Flight safety is a key issue in the aviation industry. Recently, with the prevalence of flight data recording systems, some deep learning-based studies have been devoted to predicting safety incidents based on flight data. However, these studies, although they exhibit higher prediction accuracy, have largely neglected the interpretability analysis of safety incidents which is of great concern to airlines and pilots. To address this issue, we define flight safety prediction as a multiscale time series classification problem and propose an interpretable model named IMTCN to provide both accurate predictions and high interpretability of flight safety. First, multiple temporal convolutional networks (TCNs) are utilized to capture local representations and long effective histories from multivariate flight data. Because different flight parameters are collected with diverse sampling frequencies, multiple TCNs are used to handle these parameters separately. Then, we creatively adapt the class activation mapping (CAM) method, which has been used for interpretation in image classification, and combine it with the TCN to provide flight data interpretability. The established model can pinpoint key flight parameters and corresponding moments that contribute most to safety incidents. Experimental results on a real-world dataset with 37,943 Airbus A320 aircraft flights show that our model outperforms the baselines on the task of exceedance classification and prediction 2 seconds and 4 seconds in advance, and case studies demonstrate its superb interpretability for flight safety analysis.

Published
2024
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