Outlier detection in graphs: A study on the impact of multiple graph models

Oliveira, Guilherme Campos; Moreira, Edré; Meira, Wagner; Zimek, Arthur

doi:10.2298/csis181001010c

Cited by 7 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Semi-supervised learning aims to design the algorithms, which can use these combined instances (Zhu and Goldberg, 2009). In general, the concept of anomaly/outlier is problem-dependent and it is challenging to capture all aspects of behavior in one single metric (Campos et al , 2019). In Table 1, we presented some of the data mining-based approaches that are used for credit card fraud detection; were carried out in the literature (Abdallah et al , 2016; Kültür and Çağlayan, 2017; West and Bhattacharya, 2016; Singh and Jain, 2020).…”

Section: Credit Card Fraud Detection Methodsmentioning

confidence: 99%

Credit card fraud detection using asexual reproduction optimization

Ghahfarokhi

Mansouri

Moghaddam

et al. 2021

View full text Add to dashboard Cite

PurposeThe best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO.Design/methodology/approachIn this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance, it will be the next parent, and the current parent becomes obsolete. Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.FindingsResults showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure.Originality/valueIn this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.

show abstract

Section: Credit Card Fraud Detection Methodsmentioning

confidence: 99%

Credit card fraud detection using asexual reproduction optimization

Ghahfarokhi

Mansouri

Moghaddam

et al. 2021

View full text Add to dashboard Cite

show abstract

“…An interactive approach was used in the proposed model of [8] in order to handle anomaly detection in attributed graphs. Different graph models were used in the suggested model of [4] for anomaly detection. They also used ConOut [23] and Radar [15] as their outlier detection algorithms on various graph datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Z logvar = H T W zvar + b zvar (4) Then, the model uses a sampling layer for creating the latent vector which is the low-dimensional representation of the input vector, and the decoder tries to reconstruct the input using this vector.…”

Section: Step 2: Anomaly Detectionmentioning

confidence: 99%

Unsupervised Anomaly Detection on Node Attributed Networks: A Deep Learning Approach

Kavehzadeh

Samadi

Haeri

2021

2021 the 4th International Conference on Information Science and Systems

View full text Add to dashboard Cite

Anomaly detection has been one of the important issues in social network analysis in recent years due to the crucial role it plays in different applications such as fraud and spammer detection. Using both graph and node characteristics leads to more accurate results in detecting anomalous nodes of node attributed networks. Most of the research works in this field are concentrated on supervised methods for anomaly detection. However, in real-world problems, there is not enough labeled data to use supervised methods for anomaly detection. This paper proposes an unsupervised method for detecting anomalous nodes in node attributed networks. The methods used a two-step deep learning approach. In the first step, structural features of the network are extracted using node2vec; in the next step, Variational AutoEncoder (VAE) is used to detect the anomalies considering both structural and node attributes. The anomalous nodes are recognized by their higher reconstruction loss. Our experimental results on two datasets, BlogCatalog and Flickr, show that the suggested method can compete with the state-of-theart approaches of anomaly detection in attributed networks. Our method (Deep 2 NAD) outperforms the state-of-the-art result on the Flickr dataset based on AUC. Moreover, it receives an acceptable AUC over the BlogCatalog dataset in comparison to the state-ofthe-art methods.

show abstract

“…For example, Li et al [23] compared temporal information of data set with historical similarity to detect outliers. Campos et al [24] used events to establish node and connection relationships to obtain the characteristics of outliers by creating event linkage graphs. Sun et al [25] effectively detected the outliers of spatiotemporal data by re-describing the traffic state with the innovative firefly algorithm-based spatiotemporal outlier detection method (IFA-STODM).…”

Section: Pattern-based Approachesmentioning

confidence: 99%

A highly efficient framework for outlier detection in urban traffic flow

Wang

Zeng

Zou

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

The outliers in traffic flow represent the anomalies or emergencies in the road. The detection and research of outliers will help to reveal the mechanism of such events. Aiming at the problem of outlier detection in urban traffic flow, this paper innovatively proposes a highly efficient traffic outlier detection framework based on the study of road traffic flow patterns. The main research works are as follows: (1) data pre-processing, the road traffic flow matrix of the roads is calculated based on the collected GPS data, the non-negative matrix factorisation algorithm is chosen to reduce the dimension of the matrix. (2) Road traffic flow pattern extraction, the fuzzy C-means clustering algorithm with the Optimal k-cluster centre (K-FCM) is adopted to cluster the roads with the same road traffic flow pattern. (3) Outlier detection model training and evaluation, kernel density estimation is introduced to fit the probability density of roads traffic flow matrices which are used to train the back propagation neural network based on particle swarm optimisation to obtain the outlier detection and evaluation model, and a threshold is introduced to optimise the precision and recall of the model. The experimental results show that: the average precision and recall of the proposed method in this paper are 95.38% and 96.23%, respectively, and the average detection time is 28.4 seconds. The method has high accuracy, high efficiency and good practical significance.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Outlier detection in graphs: A study on the impact of multiple graph models

Cited by 7 publications

References 36 publications

Credit card fraud detection using asexual reproduction optimization

Credit card fraud detection using asexual reproduction optimization

Unsupervised Anomaly Detection on Node Attributed Networks: A Deep Learning Approach

A highly efficient framework for outlier detection in urban traffic flow

Contact Info

Product

Resources

About