Gadbench: Revisiting and benchmarking supervised graph anomaly detection
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …
Graph based anomaly detection and description: a survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …
such as security, finance, health care, and law enforcement. While numerous techniques …
Rolx: structural role extraction & mining in large graphs
K Henderson, B Gallagher, T Eliassi-Rad… - Proceedings of the 18th …, 2012 - dl.acm.org
Given a network, intuitively two nodes belong to the same role if they have similar structural
behavior. Roles should be automatically determined from the data, and could be, for …
behavior. Roles should be automatically determined from the data, and could be, for …
Spotlight: Detecting anomalies in streaming graphs
How do we spot interesting events from e-mail or transportation logs? How can we detect
port scan or denial of service attacks from IP-IP communication data? In general, given a …
port scan or denial of service attacks from IP-IP communication data? In general, given a …
It's who you know: graph mining using recursive structural features
Given a graph, how can we extract good features for the nodes? For example, given two
large graphs from the same domain, how can we use information in one to do classification …
large graphs from the same domain, how can we use information in one to do classification …
DeltaCon Principled Massive-Graph Similarity Function with Attribution
How much has a network changed since yesterday? How different is the wiring of Bob's
brain (a left-handed male) and Alice's brain (a right-handed female), and how is it different …
brain (a left-handed male) and Alice's brain (a right-handed female), and how is it different …
Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach
Given a dynamic graph stream, how can we detect the sudden appearance of anomalous
patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can …
patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can …
A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]
M Salehi, L Rashidi - ACM SIGKDD Explorations Newsletter, 2018 - dl.acm.org
Traditionally most of the anomaly detection algorithms have been designed
for'static'datasets, in which all the observations are available at one time. In non-stationary …
for'static'datasets, in which all the observations are available at one time. In non-stationary …
F-fade: Frequency factorization for anomaly detection in edge streams
Edge streams are commonly used to capture interactions in dynamic networks, such as
email, social, or computer networks. The problem of detecting anomalies or rare events in …
email, social, or computer networks. The problem of detecting anomalies or rare events in …
Ranking causal anomalies via temporal and dynamical analysis on vanishing correlations
Modern world has witnessed a dramatic increase in our ability to collect, transmit and
distribute real-time monitoring and surveillance data from large-scale information systems …
distribute real-time monitoring and surveillance data from large-scale information systems …