How to predict crime — informatics-inspired approach from link prediction

Nora Assouli, Khelifa Benahmed and Brahim Gasbaoui

Physica A: Statistical Mechanics and its Applications, 2021, vol. 570, issue C

Abstract: Many social complex networks are best modeled as a bipartite graph and they evolve during time, thus, predicting links that will appear in them have become highly relevant and critical. Link Prediction is a key direction in social complex network research refers to estimating the possibility of the existence of non-existent links between node pairs. In criminal networks, link prediction can provide a very efficient way in the discovery of missing or hidden links and the detection of the underground groups of criminals. Only few works address the bipartite case, though, despite its high practical interest and the specific challenges it raises. Likewise, most of prior algorithms of link prediction consider a threshold. However, it is difficult to set such a proper threshold in advance for a given dataset. Hence, in this paper, we propose a new method called Latent Link Prediction based on Internal and Local Links (LLPIL) for bipartite networks. LLPIL is based on new proposed topological metric named reliability that can reflect the likelihood of two nodes to be connected. We exploit the proposed model to identifying and preventing future criminal activities. Extensive simulations show that our proposed algorithm has high prediction accuracy and low time complexity.

Keywords: Link prediction; Bipartite graph; Internal links; Similarity measures; Criminal activity prediction (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437121000674
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:570:y:2021:i:c:s0378437121000674

DOI: 10.1016/j.physa.2021.125795

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().