Dynamic Network Embedding Survey

Xue, Guotong; Zhong, Ming; Li, Jianxin; Chen, Jia; Zhai, Chengshuai; Kong, Ruochen

doi:10.48550/arxiv.2103.15447

Cited by 7 publications

(5 citation statements)

References 55 publications

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“…Many previous studies show that LSTM model has achieved incredible success in many time series application, compared to conventional RNN, including natural language processing, speech recognition, financial forecasting, etc [17,18]. Although there are other types of deep learning models, like graph convolutional networks [40,47] and representation learning techniques [4,28], they are designed for processing the data with complex structures, systems and even interaction information from different sources.…”

Section: Deep Learning Forecasting Models For Time Seriesmentioning

confidence: 99%

Clustering-enhanced stock price prediction using deep learning

Zhu

Shen

et al. 2022

World Wide Web

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In recent years, artificial intelligence technologies have been successfully applied in time series prediction and analytic tasks. At the same time, a lot of attention has been paid to financial time series prediction, which targets the development of novel deep learning models or optimize the forecasting results. To optimize the accuracy of stock price prediction, in this paper, we propose a clustering-enhanced deep learning framework to predict stock prices with three matured deep learning forecasting models, such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The proposed framework considers the clustering as the forecasting pre-processing, which can improve the quality of the training models. To achieve the effective clustering, we propose a new similarity measure, called Logistic Weighted Dynamic Time Warping (LWDTW), by extending a Weighted Dynamic Time Warping (WDTW) method to capture the relative importance of return observations when calculating distance matrices. Especially, based on the empirical distributions of stock returns, the cost weight function of WDTW is modified with logistic probability density distribution function. In addition, we further implement the clustering-based forecasting framework with the above three deep learning models. Finally, extensive experiments on daily US stock price data sets show that our framework has achieved excellent forecasting performance with overall best results for the combination of Logistic WDTW clustering and LSTM model using 5 different evaluation metrics.

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Section: Deep Learning Forecasting Models For Time Seriesmentioning

confidence: 99%

Clustering-enhanced stock price prediction using deep learning

Zhu

Shen

et al. 2022

World Wide Web

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“…The modern decision-making system is generally empowered by various deep neural network-based intelligent models that include two components: embedding and decision. The embedding component could be a backbone is composed of CNN [16,53], GCN [30,58], RNN/LSTM [12,64], or Self-attention/Transformer modules [46,55] to transform the multi-modal data, such as images, graphs, time series, sequential behavior and texts, into numeric vectors for further processing. The decision component usually takes a multi-layer fully connected neural network to model the complex relationship between inputs and decisions (outputs).…”

Section: Introductionmentioning

confidence: 99%

Multi-center federated learning: clients clustering for better personalization

et al. 2022

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Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models’ parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository (GitHub repository: https://github.com/mingxuts/multi-center-fed-learning).

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“…Even if two exercises have the same concept, the difference in their difficulty level may ultimately lead to a large difference in the probability of them being answered correctly. Therefore, some previous works [8,9,10,11,12,13] have attempted to use exercise features as a supplement to concept input, achieving success to some extent. However, due to the relatively large difference between the number of exercises and the number of exercises students actually interact with, each student may only interact with a very small fraction of the exercises, leading to problems of sparse data.…”

Section: Introductionmentioning

confidence: 99%

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

Song¹,

Li²,

Liu³

et al. 2022

Preprint

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The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students' learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. However, these efforts suffer from at least one of the following drawbacks: 1) they pay too much attention to details of the nodes rather than to high-level semantic information; 2) they struggle to effectively establish spatial associations and complex structures of the nodes; and 3) they represent either concepts or exercises only, without integrating them. Inspired by recent advances in self-supervised learning, we propose a Bi-Graph Contrastive Learning based Knowledge Tracing (Bi-CLKT) to address these limitations. Specifically, we design a two-layer comparative learning scheme based on an "exercise-to-exercise" (E2E) relational subgraph. It involves node-level contrastive learning of subgraphs to obtain discriminative representations of exercises, and graph-level contrastive learning to obtain discriminative representations of concepts. Moreover, we designed a joint contrastive loss to obtain better representations and hence better prediction performance. Also, we explored two different variants, using RNN and memory-augmented neural networks as the prediction layer for comparison to obtain better representations of exercises and concepts respectively. Extensive experiments on four real-world datasets show that the proposed Bi-CLKT and its variants outperform other baseline models.

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Dynamic Network Embedding Survey

Cited by 7 publications

References 55 publications

Clustering-enhanced stock price prediction using deep learning

Clustering-enhanced stock price prediction using deep learning

Multi-center federated learning: clients clustering for better personalization

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

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