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Most existing log anomaly detection methods take a log event count matrix or log event sequences as input, exploiting quantitative and\/or sequential relationships between log events to detect anomalies. However, only considering quantitative or sequential relationships may result in low detection accuracy. To alleviate this problem, we propose a graph-based method for unsupervised log anomaly detection, dubbed\n                    <jats:italic>Logs2Graphs<\/jats:italic>\n                    , which first converts event logs into attributed, directed, and weighted graphs, and then leverages graph neural networks to perform graph-level anomaly detection. Specifically, we introduce One-Class Digraph Inception Convolutional Networks, abbreviated as OCDiGCN, a novel graph neural network model for detecting graph-level anomalies in a collection of attributed, directed, and weighted graphs. By integrating graph representation and anomaly detection, OCDiGCN learns a specialized representation that leads to high detection accuracy. Crucially, we furnish a concise set of nodes pivotal in OCDiGCN\u2019s prediction as explanations for each detected anomaly, offering valuable insights for subsequent root cause analysis. Experiments on five benchmark datasets show that\n                    <jats:italic>Logs2Graphs<\/jats:italic>\n                    exhibits comparable or superior performance when compared to state-of-the-art log anomaly detection methods.\n                  <\/jats:p>","DOI":"10.1007\/s10618-026-01235-6","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:26:41Z","timestamp":1783067201000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph neural networks based log anomaly detection and explanation"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1124-5778","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7014-0805","authenticated-orcid":false,"given":"Jiayang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0510-3549","authenticated-orcid":false,"given":"Matthijs","family":"van Leeuwen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"1235_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3_1","volume-title":"An introduction to outlier analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal CC, Aggarwal CC (2017) An introduction to outlier analysis. 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