Popularity Prediction of Online Contents via Cascade Graph and Temporal Information

Shang, Yingdan; Zhou, Bin; Wang, Ye; Li, Aiping; Chen, Kai; Song, Yichen; Lin, Changjian

doi:10.3390/axioms10030159

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…The LSTM is an improvement on the recurrent neural network, which is often applied to time series, such as speech recognition or natural language processing. Gradient vanishing is a problem in long time series, in which the weights of neural networks do not update well [20]. The LSTM can eliminate this problem by adjusting the amount of information within a cell through the forget gate, input gate, and output gate.…”

Section: Using An Lstm Neural Network To Identify the Type Of Faultmentioning

confidence: 99%

Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method

Jafari

Lopes

2023

Axioms

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A new fault detection and identification approach is proposed. The kernel principal component analysis (KPCA) is first applied to the data for reducing dimensionality, and the occurrence of faults is determined by means of two statistical indices, T2 and Q. The K-means clustering algorithm is then adopted to analyze the data and perform clustering, according to the type of fault. Finally, the type of fault is determined using a long short-term memory (LSTM) neural network. The performance of the proposed technique is compared with the principal component analysis (PCA) method in early detecting malfunctions on a continuous stirred tank reactor (CSTR) system. Up to 10 sensor faults and other system degradation conditions are considered. The performance of the LSTM neural network is compared with three other machine learning techniques, namely the support vector machine (SVM), K-nearest neighbors (KNN) algorithm, and decision trees, in determining the type of fault. The results indicate the superior performance of the suggested methodology in both early fault detection and fault identification.

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Section: Using An Lstm Neural Network To Identify the Type Of Faultmentioning

confidence: 99%

Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method

Jafari

Lopes

2023

Axioms

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“…Inspired by the successful application of deep learning methods in natural language processing, image processing, and other fields, more researchers use deep learning models to automatically make predictions based on the representation of information diffusion features [33][34][35]. Existing deep learning-based approaches can avoid time-consuming feature engineering and have achieved significant improvement in prediction accuracy.…”

Section: Background and Related Workmentioning

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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“…In particular, infGNN generated node representation by considering both the local and global influence contexts and using gated mechanism. Shang et al [121] proposed a novel framework, which incorporates the GraphSAGE and LSTM methods. To concentrate on important nodes and exclude noises caused by other less relevant nodes, they learned importance coefficients for nodes and adopted sampling mechanism in graph pooling process.…”

Section: F Gnn-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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Popularity Prediction of Online Contents via Cascade Graph and Temporal Information

Cited by 12 publications

References 34 publications

Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method

Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method

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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