Niu, Chaoxi
Pang, Guansong
Chen, Ling
Chapter History
First Online: 17 September 2023
Ethical Statement
: In this work, we study the problem of graph-level anomaly detection which aims to identify abnormal graphs that exhibit unusual patterns in comparison to the majority in a graph set. Since graphs are widely used in various domains, anomaly detection on graphs has broad applications, such as identifying toxic molecules from chemical compound graphs and recognizing abnormal internet activity graphs. To capture the hierarchical normal patterns of graph data, we propose hierarchical memory networks to learn node and graph memory modules. The proposed method enables the detection of both locally and globally anomalous graphs. For all the used data sets in this paper, there is no private personally identifiable information or offensive content. However, when using the proposed method for solving realistic problems, it is essential to ensure that appropriate measures are taken to protect the privacy of individuals. This may include anonymizing data, limiting access to sensitive information, or obtaining informed consent from individuals before collecting their data.
Conference Information
Conference Acronym: ECML PKDD
Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Conference City: Turin
Conference Country: Italy
Conference Year: 2023
Conference Start Date: 18 September 2023
Conference End Date: 22 September 2023
Conference Number: 23
Conference ID: ecml2023
Conference URL: https://2023.ecmlpkdd.org/
Peer Review Information (provided by the conference organizers)
Type: Double-blind
Conference Management System: CMT
Number of Submissions Sent for Review: 829
Number of Full Papers Accepted: 196
Number of Short Papers Accepted: 0
Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.
Average Number of Reviews per Paper: 3.63
Average Number of Papers per Reviewer: 4.5
External Reviewers Involved: Yes
Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.