scholarly journals Information Cascades Prediction With Graph Attention

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhihao Chen ◽  
Jingjing Wei ◽  
Shaobin Liang ◽  
Tiecheng Cai ◽  
Xiangwen Liao
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Recurrent Neural Network ◽  
Information Diffusion ◽  
Diffusion Path ◽  
Information Cascades ◽  
Potential Relationship ◽  
Performance Limitation ◽  
Real World Datasets ◽  
Relationship Network

The cascades prediction aims to predict the possible information diffusion path in the future based on cascades of the social network. Recently, the existing researches based on deep learning have achieved remarkable results, which indicates the great potential to support cascade prediction task. However, most prior arts only considered either cascade features or user relationship network to predict cascade, which leads to the performance limitation because of the lack of unified modeling for the potential relationship between them. To that end, in this paper, we propose a recurrent neural network model with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features. Specifically, for user spatial feature, we learn potential relationship among users based on social network through graph attention network. Then, for temporal feature, a recurrent neural network is built to learn their structural context in several different time intervals based on timestamp with a time-decay attention. Finally, we predict the next user with the latest cascade representation which obtained by above method. Experimental results on two real-world datasets show that our model achieves better performance than the baselines on the both evaluation metrics of HITS and mean average precision.

scholarly journals Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis

2018 ◽  
Author(s):  
Guolei Yang ◽  
Ying Cai ◽  
Chandan K Reddy
Keyword(s):  
Neural Network ◽  
Survival Analysis ◽  
Recurrent Neural Network ◽  
Censored Regression ◽  
Event Time ◽  
Time Prediction ◽  
Spatio Temporal ◽  
Latent Representations ◽  
Real World Datasets ◽  
Prediction Techniques

We introduce a novel check-in time prediction problem. The goal is to predict the time a user will check-in to a given location. We formulate check-in prediction as a survival analysis problem and propose a Recurrent-Censored Regression (RCR) model. We address the key challenge of check-in data scarcity, which is due to the uneven distribution of check-ins among users/locations. Our idea is to enrich the check-in data with potential visitors, i.e., users who have not visited the location before but are likely to do so. RCR uses recurrent neural network to learn latent representations from historical check-ins of both actual and potential visitors, which is then incorporated with censored regression to make predictions. Experiments show RCR outperforms state-of-the-art event time prediction techniques on real-world datasets.

scholarly journals SAFE: A Neural Survival Analysis Model for Fraud Early Detection

2019 ◽  
Vol 33 ◽  
pp. 1278-1285
Author(s):  
Panpan Zheng ◽  
Shuhan Yuan ◽  
Xintao Wu
Keyword(s):  
Neural Network ◽  
Survival Analysis ◽  
Early Detection ◽  
Recurrent Neural Network ◽  
Training Data ◽  
Analysis Model ◽  
Detection Model ◽  
Neural Survival ◽  
Real World Datasets ◽  
Fraudulent Activity

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-theart fraud early detection approaches.

Influence maximisation in social networks: A target-oriented estimation

2018 ◽  
Vol 44 (5) ◽  
pp. 671-682 ◽  
Author(s):  
Yun-Yong Ko ◽  
Dong-Kyu Chae ◽  
Sang-Wook Kim
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Seed Set ◽  
Potential Influence ◽  
Influence Spread ◽  
Real World Datasets ◽  
Novel Target ◽  
Individual Node

Influence maximisation (IM) is the problem of finding a set of k-seed nodes that could maximize the amount of influence spread in a social network. In this article, we point out that the existing methods are taking the source-oriented estimation (SOE), which is the main reason of their failure in accurately estimating the amount of potential influence spread of an individual node. We propose a novel target-oriented estimation (TOE) that understands information diffusion more accurately as well as remedies the drawback of the existing methods. Our extensive experiments on four real-world datasets demonstrate that our proposed method outperforms the existing methods consistently with respect to the quality of the selected seed set.

scholarly journals An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 83-90
Author(s):  
Qing Guo ◽  
Zhu Sun ◽  
Jie Zhang ◽  
Yin-Leng Theng
Keyword(s):  
Neural Network ◽  
Random Walk ◽  
Recurrent Neural Network ◽  
Real World ◽  
State Of The Art ◽  
Knowledge Graph ◽  
User Mobility ◽  
Location Recommendation ◽  
Meta Path ◽  
Real World Datasets

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

scholarly journals Cascade Dynamics Modeling with Attention-based Recurrent Neural Network

2017 ◽  
Author(s):  
Yongqing Wang ◽  
Huawei Shen ◽  
Shenghua Liu ◽  
Jinhua Gao ◽  
Xueqi Cheng
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Threshold Model ◽  
Chain Structure ◽  
Viral Marketing ◽  
Dynamics Modeling ◽  
Linear Threshold ◽  
Real World Datasets ◽  
A Chain ◽  
Sequential Models

An ability of modeling and predicting the cascades of resharing is crucial to understanding information propagation and to launching campaign of viral marketing. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models, e.g., independent cascade model and linear threshold model. Recently, researchers attempt to circumvent the problem of cascade prediction using sequential models (e.g., recurrent neural network, namely RNN) that do not require knowing the underlying diffusion model. Existing sequential models employ a chain structure to capture the memory effect. However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade---one sharing behavior could be triggered by its non-immediate predecessor in the memory chain. In this paper, we propose to an attention-based RNN to capture the cross-dependence in cascade. Furthermore, we introduce a \emph{coverage} strategy to combat the misallocation of attention caused by the memoryless of traditional attention mechanism. Extensive experiments on both synthetic and real world datasets demonstrate the proposed models outperform state-of-the-art models at both cascade prediction and inferring diffusion tree.

scholarly journals What to Do Next: Modeling User Behaviors by Time-LSTM

2017 ◽  
Author(s):  
Yu Zhu ◽  
Hao Li ◽  
Yikang Liao ◽  
Beidou Wang ◽  
Ziyu Guan ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Language Modeling ◽  
Sequential Data ◽  
Sequential Order ◽  
Short Term ◽  
Time Intervals ◽  
User Behaviors ◽  
Real World Datasets

Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users' actions are of significant importance in capturing the relations of users' actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users' sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users' short-term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.

scholarly journals A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario

2020 ◽  
Vol 34 (04) ◽  
pp. 3717-3724 ◽  
Author(s):  
Monidipa Das ◽  
Mahardhika Pratama ◽  
Jie Zhang ◽  
Yew Soon Ong
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Data Stream ◽  
Mapping Function ◽  
Network Capacity ◽  
Current Data ◽  
Network Parameter ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Real World Datasets

Stream classification models for non-stationary environments often assume the immediate availability of data labels. However, in a practical scenario, it is quite natural that the data labels are available only after some temporal lag. This paper explores how a stream classifier model can be made adaptive to such label latency scenario. We propose SkipE-RNN, a self-evolutionary recurrent neural network with dynamically evolving skipped-recurrent-connection for the best utilization of previously observed label information while classifying the current data. When the data label is unavailable, SkipE-RNN uses an auto-learned mapping function to find the best match from the already known data labels and updates the network parameter accordingly. Later, upon availability of true data label, if the previously mapped label is found to be incorrect, SkipE-RNN employs a regularization technique along with the parameter updating process, so as to penalize the model. In addition, SkipE-RNN has inborn power of self-adjusting the network capacity by growing/pruning hidden nodes to cope with the evolving nature of data stream. Rigorous empirical evaluations using synthetic as well as real-world datasets reveal effectiveness of SkipE-RNN in both finitely delayed and infinitely delayed data label scenarios.

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