Danai Koutra

TL;DR: This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs, and gives a general framework for the algorithms categorized under various settings.

TL;DR: A comprehensive survey of the state-of-the-art methods for anomaly detection in data represented as graphs can be found in this article, where the authors highlight the effectiveness, scalability, generality, and robustness aspects of the methods.

TL;DR: This paper proposes RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from general network data, and compares network role discovery with network community discovery.

TL;DR: This work identifies a set of key designs -- ego- and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations -- that boost learning from the graph structure under heterophily and combines them into a graph neural network, H2GCN, which is used as the base method to empirically evaluate the effectiveness of the identified designs.

TL;DR: This work focuses on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data, but as real‐world networks are constantly changing, there has been a shift in focus to dynamic graphs,Which evolve over time.