Information cascades prediction with attention neural network

Liu, Yun; Bao, Zemin; Zhang, Zhenjiang; Tang, Di; Xiong, Fei

doi:10.1186/s13673-020-00218-w

Cited by 20 publications

(7 citation statements)

References 39 publications

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“…e last layer parameters can be trained directly as they directly impact the loss function but to train the weights of previous layers the feedback needs to travel backwards [27].…”

Section: E Error Back Propagationmentioning

confidence: 99%

“…Between the input and hidden layer weights are W 1 and between the hidden layer and output layer weights are W 2. The last layer parameters can be trained directly as they directly impact the loss function but to train the weights of previous layers the feedback needs to travel backwards [ 27 ]. The sequence in which weights will be updated is from W 2 to W 1.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Rouf

Malik

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.

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“…e last layer parameters can be trained directly as they directly impact the loss function but to train the weights of previous layers the feedback needs to travel backwards [27].…”

Section: E Error Back Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Rouf

Malik

Sharma

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Following previous works [11,51], we adopt the mean square log-transformed error (MSLE) and mean absolute error (MAE) as our evaluation metrics to measure the difference between the actual value and the predicted value, which are defined as…”

Section: Datasetsmentioning

confidence: 99%

Predicting the Popularity of Online Content by Modeling the Social Influence and Homophily Features

et al. 2022

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Predicting the popularity of online content on social network can bring considerable economic benefits to companies and marketers, and it has wide application in viral marketing, content recommendation, early warning of social unrest, etc. The diffusion process of online contents is often a complex combination of both social influence and homophily; however, existing works either only consider the social influence or homophily of early infected users and fail to model the joint effect of social influence and homophily when predicting future popularity. In this study, we aim to develop a framework to unify the social influence and homophily in popularity prediction. We use an unsupervised graph neural network framework to model nondirectional social homophily and integrate the attention mechanism with the graph neural network framework to learn the directional and heterogeneous social relationship for generating social influence representation. On the other hand, existing research studies often overlook the social group characteristics of early infected users, and we try to divide users into different social groups based on user interest and learn the social group representation from clusters. We integrate the social influence, homophily, and social group representation of early infected users to make popularity predictions. Experiments on real datasets show that the proposed method significantly improves the prediction accuracy compared with the latest methods, which confirms the importance of joint model social influence and homophily and shows that social group characteristic is an important predictor in the popularity prediction task.

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“…Lit et al [115] proposed an end-to-end framework, which incorporates Bi-GRU and AM to make the coarse-grained macro-level prediction. An attention mechanism, involving intra-attention and inter-gate modules, was designed to efficiently capture and fuse the structural and temporal information from the observed period of the cascade.…”

Section: G Rnn-based Modelsmentioning

confidence: 99%

Graph Representation Learning for Popularity Prediction Problem: A Survey

Chen,

Guo,

2022

Preprint

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The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really fast in last decade and have been one of the most effective platforms for people to communicate and share information with each other. Due to the "word of mouth" effects, information usually can spread rapidly on these social media platforms. Therefore, it is important to study the mechanisms driving the information diffusion and quantify the consequence of information spread. A lot of efforts have been focused on this problem to help us better understand and achieve higher performance in viral marketing and advertising. On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models. Compared with traditional models, GRL methods are often shown to be more effective. In this paper, we present a comprehensive review for recent works leveraging GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods. Deep learning method is further classified into six small classes: convolutional neural networks, graph convolutional networks, graph attention networks, graph neural networks, recurrent neural networks, and reinforcement learning. We compare the performance of these different models and discuss their strengths and limitations. Finally, we outline the challenges and future chances for popularity prediction problem.Impact Statement-Social platforms have exploded in popularity in last decade, and information diffusion on social networks has attracted widespread attention in both industrials and academics. Companies need estimate whether marketing strategies will be successful before real implementation to save costs and earn more profits. On the other hand, it is vital to predict diffusion of misinformation before it spreads too far, and block its further diffusion. Therefore, it is important to understand underlying diffusion dynamics of cascade and its popularity trend. With recent achievements of graph representation learning in artificial intelligence and other fields, many works leveraged graph representation learning methods for information diffusion modeling and popularity prediction. This article presents a comprehensive review of recent works using graph representation learning methods for popularity prediction. Reviewing different methods can help researchers identify strengths and limitations of existing models, and find out challenges and directions for popularity prediction in future work.

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Information cascades prediction with attention neural network

Cited by 20 publications

References 39 publications

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Predicting the Popularity of Online Content by Modeling the Social Influence and Homophily Features

Graph Representation Learning for Popularity Prediction Problem: A Survey

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