Anomaly detection in dynamic graphs using MIDAS-R
Real world networks are dynamic in nature and are constantly changing.
Contributions of Midas:-
* Streaming approach
* Theoretical Gurantee
* Effectiveness
Microcluster based detector of anomalies in Edge streams (MIDAS) performs detections by considering the temporal nature of the networks
and by considering micro-clusters instead of individual edges
.
MIDAS considers Temporal nature
Static graphs do not capture the temporal relations.
MIDAS considers micro-clusters instead of individual edges
MIDAS monitors suddenly appearing bursts of activity sharing several nodes or edges that are close by in spatial locality
Theoretical guarantees on the false positive probability
MIDAS can give binary decisions upto a user defined threshold
.