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Found 13 papers, 12 papers with code
Streaming Anomaly Detection
We then extend the count-min sketch to a Higher-Order sketch to capture complex relations in graph data, and to reduce detecting suspicious dense subgraph problem to finding a dense submatrix in constant time.
Adaptive Data Augmentation on Temporal Graphs
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
SSMF: Shifting Seasonal Matrix Factorization
In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events?
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not.
Sketch-Based Anomaly Detection in Streaming Graphs
This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).
MemStream: Memory-Based Streaming Anomaly Detection
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities?
Isconna: Streaming Anomaly Detection with Frequency and Patterns
Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records.
Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier.
AugSplicing: Synchronized Behavior Detection in Streaming Tensors
Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.
ExGAN: Adversarial Generation of Extreme Samples
Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples.
Real-Time Anomaly Detection in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?
Ranked #1 on
Intrusion Detection
on CIC-DDoS
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
Ranked #1 on
Anomaly Detection in Edge Streams
on Darpa
