Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction.

The ability to model the information diffusion process and predict its size is crucial to understanding information propagation mechanism and is useful for many applications such as popularity prediction and fake news detection. Recent research works have attempted to address the problem of information cascade prediction using two basic paradigms: (1) sequential methods, e.g., recurrent neural networks (RNNs), and (2) graph learning techniques to retain the topological information and enable consideration of structural relationships among diffusion participants. However, existing models consider the topological and temporal features separately, falling short of simulating their entanglement in the diffusion process. As a consequence, since the evolving directed acyclic graph (DAG) of information diffusion is intrinsically coupled with both topological and temporal dependencies, there is a loss of cross-domain information. In this paper, we propose a transformer enhanced Hawkes process ( Hawkesformer ), which links the hierarchical attention mechanism to Hawkes self-exciting point process for information cascade prediction. Specifically, we extend traditional Hawkes process with a topological horizon and efficiently acquire knowledge from continuous-time domain. A two-level attention architecture is used to parameterize the intensity function of Hawkesformer. At the first-level, we disentangle the primary and non-primary paths to simulate the coupled topological and temporal information for capturing the global dependencies between the nodes in a graph. At the second-level, a local pooling attentive module is proposed to embed the cascade evolution rate for modeling short-term outbreaks. Extensive experiments on two real-world datasets demonstrate the significant performance improvements of Hawkesformer over existing state-of-the-art models. • A novel coupled topological–temporal information cascade diffusion framework. • Extending the traditional Hawkes process with a topological horizon by a two-level attention architecture. • A learnable position-wise user embedding method for unattributed information directed acyclic graph. • A decoupling training scheme to cope with the long-tailed distribution in cascade sizes. 

Transformer-enhanced Hawkes process with decoupling training for information cascade prediction

Predicting information cascade popularity is a fundamental problem in social networks. Capturing temporal attributes and cascade role information (e.g., cascade graphs and cascade sequences) is necessary for understanding the information cascade. Current methods rarely focus on unifying this information for popularity predictions, which prevents them from effectively modeling the full properties of cascades to achieve satisfactory prediction performances. In this paper, we propose an explicit Time embedding based Cascade Attention Network (TCAN) as a novel popularity prediction architecture for large-scale information networks. TCAN integrates temporal attributes (i.e., periodicity, linearity, and non-linear scaling) into node features via a general time embedding approach (TE), and then employs a cascade graph attention encoder (CGAT) and a cascade sequence attention encoder (CSAT) to fully learn the representation of cascade graphs and cascade sequences. We use two real-world datasets (i.e., Weibo and APS) with tens of thousands of cascade samples to validate our methods. Experimental results show that TCAN obtains mean logarithm squared errors of 2.007 and 1.201 and running times of 1.76 h and 0.15 h on both datasets, respectively. Furthermore, TCAN outperforms other representative baselines by 10.4%, 3.8%, and 10.4% in terms of MSLE, MAE, and R-squared on average while maintaining good interpretability. 

Explicit time embedding based cascade attention network for information popularity prediction

In the process of the gradual deepening of the electric power system informatization construction, a large amount of electric power data is accumulated in the electric power field (EPF). In order to effectively organize, manage and utilize the knowledge of large amounts of power data, knowledge graph (KG) technology is introduced into the field of electric power system. Firstly, the concept, classification and construction method of KG in EPF are introduced, and the key technology and difficult problems are elaborated in detail. Secondly, the typical application of KG in EPF is analyzed from three aspects: health management of electric power equipment, fault handling of power distribution network, and supervision and transaction of electric power market. Finally, the application prospect of KG in EPF is discussed, which provides the reference for further in-depth research on the application of KG technology in EPF. 

Construction and Application of Knowledge Graph in Electric Power Field

Representation Learning (RL) aims to convert data into low-dimensional and dense real-valued vectors, so as to realize reasoning in vector space. RL is one of the important research contents in the analysis of health data. This paper systematically reviews the latest research on Electronic Health Records (EHR) RL. We searched the Web of Science, Google Scholar, and Association for Computing Machinery Digital Library for papers involving EHR RL. On the basis of literature review, we propose a new taxonomy to categorize the state-of-the-art EHR RL methods into three categories: statistics learning-based RL methods, knowledge RL methods and graph RL methods. We analyze and summarize their characteristics according to the input data form and underlying learning mechanisms. In addition, we provide evaluation strategies to verify the quality of EHR representations from both intrinsic and extrinsic perspectives. Finally, we put forward three promising research directions to promote future research. Overall, this survey aims to provide a profound overview of state-of-the-art developments in the field of EHR RL and to help researchers find the most appropriate methods.

Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data becoming overwhelmed in SNICA. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. In this paper, we provide a comprehensive survey of recent progress in applying deep learning techniques for SNICA. We first introduce related concepts and summarize the advantages of deep learning technology in SNICA. Then, we propose a general framework for deep learning technology, which applies to SNICA. Then, different SNICA application scenarios in the framework are classified and discussed according to user behavior analysis, information cascade analysis, rumor detection, and social network event analysis. Finally, we discuss the limitations of current work and suggest future directions.

Deep Learning for Social Network Information Cascade Analysis: a survey

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized.

A Survey on Approaches and Applications of Knowledge Representation Learning

Joint extraction of entities and relations is an important task in the field of natural language processing and the basis of many NLP high-level tasks. However, most existing joint models cannot solve the problem of overlapping triples well. We propose an efficient end-to-end model for joint extraction of entities and overlapping relations. Firstly, the BERT pre-training model is introduced to model the text more finely. Next, We decompose triples extraction into two subtasks: head entity extraction and tail entity extraction, which solves the problem of single entity overlap in the triples. Then, We divide the tail entity extraction into three parallel extraction sub-processes to solve entity pair overlap problem of triples, that is the relation overlap problem. Finally, We transform each extraction sub-process into sequence tag task. We evaluate our model on the New York Times (NYT) dataset and achieve overwhelming results compared with most of the current models, Precise =0.870, Recall = 0.851, and F1 = 0.860. The experimental results show that our model is effective in dealing with triples overlap problem.

An Advanced BERT-Based Decomposition Method for Joint Extraction of Entities and Relations

This chapter introduces the application of information cascading analysis in social networks. We present a deep learning based framework of social network information cascade analysis, and we show the challenges of applying the MDATA model. The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data have become overwhelmed in SNICA. At the same time, the MDATA model fuses data from multiple sources in a graph, which can be applied to the SNICA problems. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. This chapter provides a comprehensive survey of recent progress in applying deep learning techniques for SNICA.

Information Cascading in Social Networks

The invention provides a false news prediction method, system and device based on deep learning and a medium, which can increase the prediction speed and can avoid the defect of long-term dependence.The method comprises the following steps: 1, preprocessing news text data; 2, mapping the preprocessed news text data into word vectors by using a BERT pre-training model; 3, constructing a predictionmodel based on the deep learning neural network, wherein the constructed prediction model comprises a feature extraction layer, an attention layer and a classifier layer; 4, obtaining an optimized prediction model through adversarial training; 5, inputting the training data into the optimization prediction model, and training the prediction model to obtain a trained prediction model; 6, inputtingnews text data to be predicted into the trained prediction model, and outputting a prediction result.

False news prediction method, system and device based on deep learning, and medium

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