Publications

Selected publications

2021

1. arXiv

   Edge rewiring goes neural: Boosting network resilience without rich features

   Shanchao Yang, Kaili Ma, Baoxiang Wang, Tianshu Yu and Hongyuan Zha

   arXiv, 2021

   Abs arXiv Bib Code

   Improving the resilience of a network is a fundamental problem in network science, which protects the underlying system from natural disasters and malicious attacks. This is traditionally achieved via successive degree-preserving edge rewiring operations, with the major limitation of being transductive. Inductively solving graph-related tasks with sequential actions is accomplished by adopting graph neural networks (GNNs) coupled with reinforcement learning under the scenario with rich graph features. However, such frameworks cannot be directly applied to resilience tasks where only pure topological structure is available. In this case, GNNs can barely learn useful information, resulting in prohibitive difficulty in making actions for successively rewiring edges under a reinforcement learning context. In this paper, we study in depth the reasons why typical GNNs cause such failure. Based on this investigation, we propose ResiNet, the first end-to-end trainable inductive framework to discover resilient network topologies while balancing network utility. To this end, we reformulate resilience optimization as an MDP equipped with edge rewiring action space, and propose a pure topology-oriented variant of GNN called filtration enhanced graph neural network (FireGNN), which can learn from graphs without rich features. Extensive experiments demonstrate that ResiNet achieves a near-optimal resilience gain on various graphs while balancing the utility, and outperforms existing approaches by a large margin.

   @article{ResiNet,
                 doi = {10.48550/ARXIV.2110.09035},
                 author = {Shanchao Yang and Kaili Ma and Baoxiang Wang and Tianshu Yuand Hongyuan  Zha},
                 keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
                 title = {Edge rewiring goes Neural: Boosting network resilience without rich features},
                 publisher = {arXiv},
                 year = {2021},
                 copyright = {arXiv.org perpetual, non-exclusive license}
                 }

2020

1. arXiv

   Learn to generate time series conditioned graphs with generative adversarial nets

   Shanchao Yang, Jing Liu, Kai Wu, and Mingming Li

   arXiv, 2020

   Abs arXiv Bib

   Deep learning based approaches have been utilized to model and generate graphs subjected to different distributions recently. However, they are typically unsupervised learning based and unconditioned generative models or simply conditioned on the graph-level contexts, which are not associated with rich semantic node-level contexts. Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series. For example, we can study the interrelationships between genes in a gene regulatory network of a certain disease conditioned on their gene expression data recorded as time series. To achieve this, we propose a novel Time Series conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of rich node-level context structures conditioning and measuring similarities directly between graphs and time series. Extensive experiments on synthetic and real-word gene regulatory networks datasets demonstrate the effectiveness and generalizability of the proposed TSGG-GAN.

   @article{TSGGAN,
                 doi = {10.48550/ARXIV.2003.01436},
                 author = {Shanchao Yang and Jing Liu and Kai Wu and Mingming Li},
                 keywords = {Machine Learning (cs.LG), Social and Information Networks (cs.SI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
                 title = {Learn to generate time series conditioned graphs with generative adversarial nets},
                 publisher = {arXiv},
                 year = {2020},
                 copyright = {arXiv.org perpetual, non-exclusive license}
                 }

2018

1. TFS

   Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform

   Shanchao Yang, and Jing Liu

   IEEE Transactions on Fuzzy Systems, 2018

   Abs Bib HTML Code

   Fuzzy cognitive maps (FCMs) have been successfully used to model and predict stationary time series. However, it still remains challenging to deal with large-scale nonstationary time series, which have trend and vary rapidly with time. In this paper, we propose a time-series prediction model based on the hybrid combination of high-order FCMs (HFCMs) with the redundant wavelet transform to handle large-scale nonstationary time series. The model is termed as Wavelet-HFCM. The redundant Haar wavelet transform is applied to decompose original nonstationary time series into multivariate time series; then, the HFCM is used to model and predict multivariate time series. In learning HFCMs to represent large-scale multivariate time series, a fast HFCM learning method is designed on the basis of ridge regression to reduce the learning time. Finally, summing multivariate time series up yields the predicted time series at each time step. Compared with existing classical methods, the experimental results on eight benchmark datasets show the effectiveness of our proposal, indicating that our prediction model can be applied to various prediction tasks.

   @article{yang2018time,
       title = {Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform},
       author = {Yang, Shanchao and Liu, Jing},
       journal = {IEEE Transactions on Fuzzy Systems},
       volume = {26},
       number = {6},
       pages = {3391--3402},
       year = {2018},
       publisher = {IEEE}
     }

All publications

2021

1. arXiv

   Edge rewiring goes neural: Boosting network resilience without rich features

   Shanchao Yang, Kaili Ma, Baoxiang Wang, Tianshu Yu and Hongyuan Zha

   arXiv, 2021

   Abs arXiv Bib Code

   Improving the resilience of a network is a fundamental problem in network science, which protects the underlying system from natural disasters and malicious attacks. This is traditionally achieved via successive degree-preserving edge rewiring operations, with the major limitation of being transductive. Inductively solving graph-related tasks with sequential actions is accomplished by adopting graph neural networks (GNNs) coupled with reinforcement learning under the scenario with rich graph features. However, such frameworks cannot be directly applied to resilience tasks where only pure topological structure is available. In this case, GNNs can barely learn useful information, resulting in prohibitive difficulty in making actions for successively rewiring edges under a reinforcement learning context. In this paper, we study in depth the reasons why typical GNNs cause such failure. Based on this investigation, we propose ResiNet, the first end-to-end trainable inductive framework to discover resilient network topologies while balancing network utility. To this end, we reformulate resilience optimization as an MDP equipped with edge rewiring action space, and propose a pure topology-oriented variant of GNN called filtration enhanced graph neural network (FireGNN), which can learn from graphs without rich features. Extensive experiments demonstrate that ResiNet achieves a near-optimal resilience gain on various graphs while balancing the utility, and outperforms existing approaches by a large margin.

   @article{ResiNet,
   doi = {10.48550/ARXIV.2110.09035},
   author = {Shanchao Yang and Kaili Ma and Baoxiang Wang and Tianshu Yuand Hongyuan  Zha},
   keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
   title = {Edge rewiring goes Neural: Boosting network resilience without rich features},
   publisher = {arXiv},
   year = {2021},
   copyright = {arXiv.org perpetual, non-exclusive license}
   }

2020

1. arXiv

   Learn to generate time series conditioned graphs with generative adversarial nets

   Shanchao Yang, Jing Liu, Kai Wu, and Mingming Li

   arXiv, 2020

   Abs arXiv Bib

   Deep learning based approaches have been utilized to model and generate graphs subjected to different distributions recently. However, they are typically unsupervised learning based and unconditioned generative models or simply conditioned on the graph-level contexts, which are not associated with rich semantic node-level contexts. Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series. For example, we can study the interrelationships between genes in a gene regulatory network of a certain disease conditioned on their gene expression data recorded as time series. To achieve this, we propose a novel Time Series conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of rich node-level context structures conditioning and measuring similarities directly between graphs and time series. Extensive experiments on synthetic and real-word gene regulatory networks datasets demonstrate the effectiveness and generalizability of the proposed TSGG-GAN.

   @article{TSGGAN,
                 doi = {10.48550/ARXIV.2003.01436},
                 author = {Shanchao Yang and Jing Liu and Kai Wu and Mingming Li},
                 keywords = {Machine Learning (cs.LG), Social and Information Networks (cs.SI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
                 title = {Learn to generate time series conditioned graphs with generative adversarial nets},
                 publisher = {arXiv},
                 year = {2020},
                 copyright = {arXiv.org perpetual, non-exclusive license}
                 }

2. KBS

   Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps

   Kaixin Yuan, Jing Liu, Shanchao Yang, Kai Wu, and Fang Shen

   Knowledge-Based Systems, 2020

   Abs Bib HTML

   Fuzzy cognitive maps (FCMs) have emerged as a powerful tool for dealing with the task of time series prediction. Most existing research devoted to designing an effective method to extract feature time series from the original time series, which are used to construct FCMs and predict the time series. However, in existing methods, all extracted feature time series, including the redundant feature time series, were used to develop FCMs instead of selecting the key feature time series (KFTS) to construct FCMs, which limits the generalization and prediction accuracy of the models. In this paper, we propose a framework based on kernel mapping and high-order FCMs (HFCM) to forecast time series inspired by the kernel methods and support vector regression (SVR). The model is termed as Kernel-HFCM. Kernel mapping is designed to map the original one-dimensional time series into multidimensional feature time series, and then the feature selection algorithm is proposed to select the KFTS from the multidimensional feature time series to develop the HFCM. Finally, reverse kernel mapping is used to map the feature time series back to the predicted one-dimensional time series. In comparison to the existing methods, the experimental results on seven benchmark datasets demonstrate the effectiveness of Kernel-HFCM in time series prediction.

   @article{yuan2020time,
       title = {Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps},
       author = {Yuan, Kaixin and Liu, Jing and Yang, Shanchao and Wu, Kai and Shen, Fang},
       journal = {Knowledge-Based Systems},
       volume = {206},
       pages = {106359},
       year = {2020},
       publisher = {Elsevier}
     }

2019

1. TFS

   Time series prediction using sparse autoencoder and high-order fuzzy cognitive maps

   Kai Wu, Jing Liu, Penghui Liu, and Shanchao Yang

   IEEE Transactions on Fuzzy Systems, 2019

   Abs Bib HTML

   The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved. Although many methods have been proposed to cope with this issue, the performance of these methods is far from satisfactory. Traditional FCM-based predictors have three limitations. First, current feature extraction operators are incapable of learning good representations of original time series. Second, current methods use just the output of FCMs to predict the next value; they do not directly utilize the important information of the latent features. Third, current FCM-based predictors optimize each component individually, thereby leading to low prediction accuracy. For example, these methods first optimize the feature extraction operator and then learn the FCMs from the latent features; they do not simultaneously optimize the whole prediction model. In this article, we develop a framework based on a sparse autoencoder (SAE) and a high-order FCM (HFCM) to address the time series prediction problem; we refer this framework as SAE-FCM. To overcome the first limitation of current methods, an SAE is employed to extract features from original time series. Unlike current FCM-based predictors, our method combines the output of both the SAE and the HFCM to calculate the predicted value, thereby overcoming the second limitation of traditional FCM-based predictors. In an application of the idea of “fine tuning” in deep learning, the weights of SAE-FCM can be updated by the batch gradient descent method if the prediction errors are great. Thus, we can optimize SAE-FCM as a whole and overcome the third limitation. We validate the performance of SAE-FCM on ten datasets. Compared with the experimental results obtained by using state-of-the-art methods, the experimental results obtained by using SAE-FCM demonstrate the effectiveness of our method. Extensive experiments also show that SAE-FCM can effectively overcome the above limitations.

   @article{wu2019time,
       author = {Wu, Kai and Liu, Jing and Liu, Penghui and Yang, Shanchao},
       journal = {IEEE Transactions on Fuzzy Systems},
       volume = {28},
       number = {12},
       pages = {3110--3121},
       year = {2019},
       publisher = {IEEE}
     }

2018

1. TFS

   Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform

   Shanchao Yang, and Jing Liu

   IEEE Transactions on Fuzzy Systems, 2018

   Abs Bib HTML Code

   Fuzzy cognitive maps (FCMs) have been successfully used to model and predict stationary time series. However, it still remains challenging to deal with large-scale nonstationary time series, which have trend and vary rapidly with time. In this paper, we propose a time-series prediction model based on the hybrid combination of high-order FCMs (HFCMs) with the redundant wavelet transform to handle large-scale nonstationary time series. The model is termed as Wavelet-HFCM. The redundant Haar wavelet transform is applied to decompose original nonstationary time series into multivariate time series; then, the HFCM is used to model and predict multivariate time series. In learning HFCMs to represent large-scale multivariate time series, a fast HFCM learning method is designed on the basis of ridge regression to reduce the learning time. Finally, summing multivariate time series up yields the predicted time series at each time step. Compared with existing classical methods, the experimental results on eight benchmark datasets show the effectiveness of our proposal, indicating that our prediction model can be applied to various prediction tasks.

   @article{yang2018time,
       title = {Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform},
       author = {Yang, Shanchao and Liu, Jing},
       journal = {IEEE Transactions on Fuzzy Systems},
       volume = {26},
       number = {6},
       pages = {3391--3402},
       year = {2018},
       publisher = {IEEE}
     }