MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02510ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125111037.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250429t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010014 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .L46 2023 r Bc. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Leong, Teng Man, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Temporal graph for fraud detection and analytics / |
| Statement of responsibility, etc. |
Leong Teng Man |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 39 pages : |
| Other physical details |
illustrations ; |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Centre for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 20223 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Representing time-based transactions as graphs offers insights for fraud detection. However, limited and confidential datasets pose challenges. To address this, the project aims to assess the quality of generative model by using the generated data in training the fraud classifier model. A generative approach using graph autoencoders is employed to augment data for fraud detection. Experiments utilize the Elliptic dataset, a Bitcoin transaction dataset with 49 connected component graphs representing confirmations at twoweek intervals. A custom variational graph autoencoder (VGAE) is developed, encountering challenges with the defined loss function, resulting in excessive edge reconstruction. Among experimented three graph neural network (GNN) models—Graph Convolutional Networks (GCN), Graph Attention Networks (GAT) and GATv2, it is found that none effectively detect unseen illicit transactions. While GAT and GATv2 outperform GCN, models trained on reconstructed data perform worse than those on purely original data. It is observed the consistent metric fluctuations among models regardless of data used during training. This indicates the VGAE captures underlying patterns. This study highlights challenges and suggests future research directions, including adversarially regularized graph autoencoders, alternative architectures, efficient loss functions, and ensemble methods. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Project |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |