scholarly journals Situation-Aware Deep Reinforcement Learning Link Prediction Model for Evolving Criminal Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16550-16559 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Muhammad Khurram Khan
Keyword(s):  
Reinforcement Learning ◽  
Prediction Model ◽  
Link Prediction ◽  
Criminal Networks

scholarly journals Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Analysis ◽  
Prediction Model ◽  
Supervised Learning ◽  
Link Prediction ◽  
Supervised Machine Learning ◽  
Criminal Networks ◽  
Criminal Network ◽  
Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities

2020 ◽  
Vol 38 (4) ◽  
pp. 1-28
Author(s):  
Richong Zhang ◽  
Samuel Mensah ◽  
Fanshuang Kong ◽  
Zhiyuan Hu ◽  
Yongyi Mao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Knowledge Base ◽  
Link Prediction ◽  
Vocabulary Knowledge

scholarly journals Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning

2020 ◽  
Vol 11 (40) ◽  
pp. 10959-10972
Author(s):  
Xiaoxue Wang ◽  
Yujie Qian ◽  
Hanyu Gao ◽  
Connor W. Coley ◽  
Yiming Mo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Value Network ◽  
Synthesis Routes

A new MCTS variant with a reinforcement learning value network and solvent prediction model proposes shorter synthesis routes with greener solvents.

Enhancing link prediction by exploring community membership of nodes

2019 ◽  
Vol 33 (31) ◽  
pp. 1950382
Author(s):  
Shenshen Bai ◽  
Shiyu Fang ◽  
Longjie Li ◽  
Rui Liu ◽  
Xiaoyun Chen
Keyword(s):  
Community Structure ◽  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Network Data ◽  
Data Link ◽  
Structure Information ◽  
Community Membership

With the proliferation of available network data, link prediction has become increasingly important and captured growing attention from various disciplines. To enhance the prediction accuracy by making full use of community structure information, this paper proposes a new link prediction model, namely CMS, in which different community memberships of nodes are investigated. In the opinion of CMS, different memberships can have different influence to link’s formation. To estimate the connection likelihood between two nodes, the CMS model weights the contribution of each shared neighbor according to the corresponding community membership. Three CMS-based methods are derived by introducing three forms of contribution that neighbors make. Extensive experiments on 12 networks are conducted to evaluate the performance of CMS-based methods. The results manifest that CMS-based methods are more effective and robust than baselines.

scholarly journals Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Guo ◽  
Kai Zhang ◽  
Xinjie Wei ◽  
Mei Liu
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Prediction Model ◽  
Network Model ◽  
Power Grid ◽  
Load Forecasting ◽  
Model Parameters ◽  
Short Term ◽  
Forecasting Method ◽  
Short Term Load Forecasting

Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error e MAPE and root-mean-square error e RMSE of the performance indexes of the prediction model are 10.01% and 2.156 MW, respectively, showing excellent curve fitting ability and load forecasting ability.

scholarly journals Dynamic portfolio rebalancing through reinforcement learning

2021 ◽  
Author(s):  
Qing Yang Eddy Lim ◽  
Qi Cao ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Prediction Model ◽  
Global Market ◽  
Alternative View ◽  
Minimal Risk ◽  
Market Conditions ◽  
Portfolio Rebalancing ◽  
Risk Levels ◽  
Portfolio Returns ◽  
Dynamic Portfolio

AbstractPortfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.

Sign in / Sign up

Export Citation Format

Share Document