@inproceedings{bea88e7aa17a43a483d562c8add9c5d8,
title = "Drop the Mask! GAMM - A Taxonomy for Graph Attributes Missing Mechanisms",
abstract = "Exploring missing data in attributed graphs introduces unique challenges beyond those found in tabular datasets. In this work, we extend the taxonomy for missing data mechanisms to attributed graphs by proposing GAMM (Graph Attributes Missing Mechanisms), a framework that systematically links missingness probability to both node attributes and the underlying graph structure. Our taxonomy enriches the conventional definitions of masking mechanisms by introducing graph-specific dependencies. We empirically demonstrate that state-of-the-art imputation methods, while effective on traditional masks, significantly struggle when confronted with these more realistic graph-aware missingness scenarios.",
keywords = "Attributed Graph, Masking Taxonomy, Missing Values Imputation, Missingness Mechanisms",
author = "Richard Serrano and Baptiste Jeudy and Charlotte Laclau and Christine Largeron",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 24th International Symposium on Intelligent Data Analysis, IDA 2026 ; Conference date: 22-04-2026 Through 24-04-2026",
year = "2026",
month = jan,
day = "1",
doi = "10.1007/978-3-032-23833-7\_22",
language = "English",
isbn = "9783032238320",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "298--311",
editor = "Mitra Baratchi and \{van Rijn\}, \{Jan N.\} and Siegfried Nijssen",
booktitle = "Advances in Intelligent Data Analysis XXIV - 24th International Symposium on Intelligent Data Analysis, IDA 2026, Leiden, Proceedings",
}