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

Lim, Marcus; Abdullah, Azween; Jhanjhi, N. Z.; Supramaniam, Mahadevan

doi:10.3390/computers8010008

Cited by 74 publications

(35 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This result is consistent with that of prior research conducted by Lim at al. on DRL link prediction based on a snapshot of a criminal network [26].…”

Section: B Results and Discussionmentioning

confidence: 99%

“…The integration of DL will have an impact on the performance of the link prediction model as it is dependent on a few factors such as optimising the use of graphics processing unit (GPU) and parallel processing. The accuracy of the DRL-CNA model in the prediction of links is evaluated using the AUC scores [26].…”

Section: Models and Methodology A Proposed Fdrl-cna Modelmentioning

confidence: 99%

“…For the purpose of this research, the proposed FDRL-CNA model and the baseline time-evolving DRL link prediction criminal network analysis (TDRL-CNA) model incorporated the concepts of the Rooted PageRank [26] where the node pairs are sorted on the basis of the weights computed according to the time that elapsed at the current instance, t, of the network topology and the instance at the time of prediction, t . If nodes x and y are neighbours, with z being any node that may exist between the two nodes, then the probability of the walk going from x to y can be denoted as follows [28]:…”

Section: Time-evolving Graphmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

Evidently, criminal network activities have shown an increasing trend in terms of complexity and frequency, particularly with the advent of social media and modern telecommunication systems. In these circumstances, law enforcement agencies have to be armed with advance criminal network analysis (CNA) tools capable of uncovering with speed, probable key hidden relationships (links/edges) and players (nodes) in order to anticipate, undermine and cripple organised crime syndicates and activities. The development of link prediction models for network orientated domains is based on Social Network Analysis (SNA) methods and models. The key objective of this research is to develop a link prediction model that incorporates a fusion of metadata (i.e. environment data sources such as arrest warrants, judicial judgement, wiretap records and police station proximity) with a time-evolving criminal dataset in order to be aware of real-world situations to improve the quality of link prediction. Based on the review of related work, most of the models are constructed by leveraging on classical machine learning (ML) techniques such as support vector machine (SVM) without metadata fusion. The problem with the use of classical ML techniques is the lack of available domain dataset which is sufficiently large for training purpose. Compared to sociaI network, criminal network dataset by nature tends to relatively much smaller. In view of this, deep reinforcement learning (DRL) technique which could improve the training of models with the self-generated dataset is leveraged upon to construct the model. In this research, a purely time-evolving DRL model (TDRL-CNA) without metadata fusion is designed as a baseline for comparison with the metadata fusion model (FDRL-CNA). The experimental results show that the predictive accuracy of new and recurrent links by the FDRL-CNA model is higher than the baseline TDRL-CNA model that does not factor data fusion from different data sources. INDEX TERMS Data fusion, time-evolving network, criminal network analysis, deep reinforcement learning, node similarity.

show abstract

“…This result is consistent with that of prior research conducted by Lim at al. on DRL link prediction based on a snapshot of a criminal network [26].…”

Section: B Results and Discussionmentioning

confidence: 99%

Section: Models and Methodology A Proposed Fdrl-cna Modelmentioning

confidence: 99%

Section: Time-evolving Graphmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The criminal network prediction models commonly rely on Social Network Analysis (SNA) metrics. These models leverage on machine learning (ML) techniques to enhance the predictive accuracy of the models and processing speed [29], this can be a great scope to conduct research [30][31][32][33][34].…”

Section: Resultsmentioning

confidence: 99%

Criminal Community Detection Based on Isomorphic Subgraph Analytics

Sangkaran

Abdullah

Jhanjhi

2020

Open Computer Science

Self Cite

View full text Add to dashboard Cite

AbstractAll highly centralised enterprises run by criminals do share similar traits, which, if recognised, can help in the criminal investigative process. While conducting a complex confederacy investigation, law enforcement agents should not only identify the key participants but also be able to grasp the nature of the inter-connections between the criminals to understand and determine the modus operandi of an illicit operation. We studied community detection in criminal networks using the graph theory and formally introduced an algorithm that opens a new perspective of community detection compared to the traditional methods used to model the relations between objects. Community structure, generally described as densely connected nodes and similar patterns of links is an important property of complex networks. Our method differs from the traditional method by allowing law enforcement agencies to be able to compare the detected communities and thereby be able to assume a different viewpoint of the criminal network, as presented in the paper we have compared our algorithm to the well-known Girvan-Newman. We consider this method as an alternative or an addition to the traditional community detection methods mentioned earlier, as the proposed algorithm allows, and will assists in, the detection of different patterns and structures of the same community for enforcement agencies and researches. This methodology on community detection has not been extensively researched. Hence, we have identified it as a research gap in this domain and decided to develop a new method of criminal community detection.

show abstract

“…This focus towards SNs, have created a lot of opportunities for researchers from different Figure 1. Example of Link Prediction in Social Network [43], identification of criminals in criminal network [59], item recommendation [99] etc. Figure 2 represents the number of published research papers with search keyword "Link Prediction" on DBLP (Computer Science Bibliography).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Link Prediction Techniques for Social Network

Samad¹,

Qadir²,

Nawaz³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

A growing trend of using social networking sites is attracting researchers to study and analyze different aspects of social network. Besides many problems, link prediction is a fascinating problem in the field of social network analysis (SNA). Link prediction, in social network analysis, is a task of identifying the missing links and predicting the new links. Several researchers have proposed solutions for the link prediction problem during the past two decades. However, there is a need to provide comprehensive overview of the significant contributions for a thorough analysis. The objective of this review is to summaries and discuss the existing link prediction algorithms in a common context for an unbiased analysis. The extensive review is presented by constructing the systematical category for proposed algorithms, selected problems, evaluation measures along with selected network datasets. Finally, applications of link prediction are discussed.

show abstract

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

Cited by 74 publications

References 12 publications

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

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

Criminal Community Detection Based on Isomorphic Subgraph Analytics

A Comprehensive Survey of Link Prediction Techniques for Social Network

Contact Info

Product

Resources

About