Table 3 Articles selected from the Scopus repository (ordered by number of citations)
No | Title/Author/Year | Citation | Journal/Conference |
|---|---|---|---|
1 | An Objective Corruption Risk Index Using Public Procurement Data (Fazekas et al. 2016) | 47 | European Journal on Criminal Policy and Research |
2 | From Corruption to State Capture: A New Analytical Framework with Empirical Applications from Hungary (Fazekas & Tóth 2016) | 41 | Political Research Quarterly |
3 | Uncovering High-Level Corruption: Cross-National Objective Corruption Risk Indicators Using Public Procurement Data (Fazekas & Kocsis 2017) | 35 | British Journal of Political Science |
4 | Internet auction fraud detection using social network analysis and classification tree approaches (Chiuet al. 2011) | 34 | International Journal of Electronic Commerce |
5 | Bid-rigging networks and state-corporate crime in the construction industry (Reeves-Latour & Morselli 2017) | 32 | Social Networks |
6 | The impact of inter-organizational relationships on contractors' success in winning public procurement projects: The case of the construction industry in the Veneto region (Sedita & Apa 2015) | 28 | International Journal of Project Management |
7 | Combining ranking concept and social network analysis to detect collusive groups in online auctions (Lin et al. 2012) | 23 | Expert Systems With Applications |
8 | Finding the needle: A risk-based ranking of product listings at online auction sites for non-delivery fraud prediction (Almendra 2013) | 19 | Expert Systems With Applications |
9 | Corruption and complexity: a scientific framework for the analysis of corruption networks (Luna-Pla & Carlock 2020) | 18 | Applied Network Science |
10 | Fuzzy rule optimization for online auction frauds detection based on genetic algorithm (Yu & Lin 2013) | 15 | Electronic Commerce Research |
11 | A network approach to cartel detection in public auction markets (Wachs & Kertesz 2019) | 14 | Scientific Reports |
12 | Social capital predicts corruption risk in towns (Wachs et al. 2019) | 13 | Royal Society Open Science |
13 | Leveraging social networks to combat collusion in reputation systems for peer-to-peer networks (Li et al. 2013) | 13 | IEEE Transactions on Computers |
14 | Distinguishing Characteristics of Corruption Risks in Iranian Construction Projects: A Weighted Correlation Network Analysis (Hosseini et al. 2019) | 12 | Science and Engineering Ethics |
15 | Network similarity and collusion (Morselli & Ouellet 2018) | 12 | Social Networks |
16 | Machine learning with screens for detecting bid-rigging cartels (Huber & Imhof 2019) | 9 | International Journal of Industrial Organization |
17 | Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques (Ganguly & Sadaoui 2018) | 8 | 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE) |
18 | A decision support system for fraud detection in public procurement (Velasco et al. 2021) | 7 | International Transactions in Operational Research |
19 | Prediction of public procurement corruption indices using machine learning methods (Rabuzin & Modrusan 2019) | 7 | 11th International Conference on Knowledge Management and Information Systems (KMIS) |
20 | Comparison of ANN Classifier to the Neuro-Fuzzy System for Collusion Detection in the Tender Procedures of Road Construction Sector (Anysz et al. 2018) | 7 | 3rd World Multidisciplinary Civil Engineering, Architecture, Urban Planning Symposium (WMCAUS) |
21 | Using Self-Organizing Maps for fraud prediction at online auction sites (Almendra & Enachescu 2014) | 7 | 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC |
22 | Detection of feedback reputation fraud in Taobao using social network theory (Zhu et al. 2011) | 7 | Proceedings—2011 International Joint Conference on Service Sciences, IJCSS 2011 |
23 | Corruption risk in contracting markets: a network science perspective (Wachs, Fazekas, & Kertész, 2021) | 6 | International Journal of Data Science and Analytics |
24 | Corruption and the network structure of public contracting markets across government change (Fazekas & Wachs 2020) | 5 | Politics and Governance |
25 | Mapping Corruption Risks in Public Procurement: Uncovering Improvement Opportunities and Strengthening Controls (Sharma et al. 2019) | 5 | Public Performance & Management Review |
26 | Big data system for analyzing risky procurement entities (Dhurandhar et Al. 2015) | 5 | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
27 | Online hybrid model for online fraud prevention and detection (Mundra & Rakesh 2014) | 5 | Advances in Intelligent Systems and Computing |
28 | Uncovering the structure of public procurement transactions (Popa 2019) | 4 | Business and Politics |
29 | Preventing rather than punishing: An early warning model of malfeasance in public procurement (Gallego et al. 2021) | 3 | International Journal of Forecasting |
30 | Clustering and Labeling Auction Fraud Data (Alzahrani & Sadaoui 2020) | 3 | International Conference on Data Management, Analytics and Innovation (ICDMAI) |
31 | Trust model based on Islamic business ethics and social network analysis (Lei et al. 2018a, b) | 3 | International Journal on Advanced Science, Engineering and Information Technology |
32 | How do strategic networks influence awarding contract? Evidence from French public procurement (Mamavi et al. 2017) | 3 | International Journal of Public Sector Management |
33 | Detecting fraudsters in online auction using variations of neighbor diversity (Khomnotai & Lin 2015) | 3 | International Journal of Engineering and Technology Innovation |
34 | Incremental collusive fraud detection in large-scale online auction networks (Dadfarnia et al. 2020) | 2 | Journal of Supercomputing |
35 | Bidder Network Community Division and Collusion Suspicion Analysis in Chinese Construction Projects (Zhu et al. 2020) | 2 | Advances in Civil Engineering |
36 | Detecting the collusive bidding behavior in below average bid auction (Lei et al. 2018a, b) | 2 | 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) |
37 | Analysis on bidding behaviours for detecting shill bidders in online auctions (Majadi et al. 2017) | 2 | Proceedings—2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International |
38 | Network Analysis for Fraud Detection in Portuguese Public Procurement (Carneiro et al. 2020) | 1 | International Conference on Intelligent Data Engineering and Automated Learning |
39 | Improving fraudster detection in online auctions by using neighbor-driven attributes (Lin & Khomnotai 2016) | 1 | Entropy |
40 | Characterization of the firm–firm public procurement co-bidding network from the State of Ceará (Brazil) municipalities (Lyra et al. 2021a, b.2) | 0 | Applied Network Science |
41 | Tackling corruption in urban infrastructure procurement: Dynamic evaluation of the critical constructs and the anti-corruption measures (Owusu et al. 2021) | 0 | Cities |
42 | A social network-based examination on bid riggers’ relationships in the construction industry: A case study of China (Xiao et al. 2021) | 0 | Buildings |
43 | Conspiracy of Corporate Networks in Corruption Scandals (Nicolás-Carlock & Luna-Pla 2021) | 0 | Frontiers in Physics |
44 | The extra-legal governance of corruption: Tracing the organization of corruption in public procurement (Fazekas et al. 2021) | 0 | Governance |
45 | Networked Corruption Risks in European Defense Procurement (Czibik et al. 2021) | 0 | Understanding Complex Systems |
46 | Corruption in the Implementation of Public Procurement from Small and Medium Businesses (Perevezentceva, et al. 2021) | 0 | Lecture Notes in Networks and Systems |
47 | Real-Time Shill Bidding Fraud Detection Empowered with Fussed Machine Learning (Abidi et al. 2021) | 0 | IEEE Access |
48 | Adaptation of cluster analysis methods in respect to vector space of social network analysis indicators for revealing suspicious government contracts (Davydenko et al. 2017) | 0 | IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud) |