What is graph based anomaly detection?

Graph-based anomaly detection (GBAD) approaches, a branch of data mining and machine learning techniques that focuses on interdependencies between different data objects, have been increasingly used to analyze relations and connectivity patterns in networks to identify unusual patterns [1].

What is a anomaly graph?

Definition 1. A graph substructure S’ is anomalous if it is not isomorphic to the graph’s normative substructure S, but is isomorphic to S within X%. X signifies the percentage of vertices and edges that would need to be changed in order for S’ to be isomorphic to S.

What are similarity graphs?

The similarity graph W encodes the information of how two target words are similar, in a distributional semantics perspective [HAR 54].

What are the characteristics of anomaly detection?

Anomaly Detector only takes in time series data by using timestamps and numbers. The service has no knowledge of the context and surroundings where the data is collected. In production use, decision makers might need to consider knowledge beyond those measures.

What is GraphSAGE?

GraphSAGE is a framework for inductive representation learning on large graphs. GraphSAGE is used to generate low-dimensional vector representations for nodes, and is especially useful for graphs that have rich node attribute information. Motivation. Code. Datasets.

What is an anomaly in data?

Anomaly detection is the identification of rare events, items, or observations which are suspicious because they differ significantly from standard behaviors or patterns. Anomalies in data are also called standard deviations, outliers, noise, novelties, and exceptions.

Why do we use temperature anomaly?

Temperature anomalies are useful for deriving average surface temperatures because they tend to be highly correlated over large distances (of the order of 1000 km). In other words, anomalies are representative of temperature changes over large areas and distances.

How do you find the similarity of data?

To calculate the similarity between two examples, you need to combine all the feature data for those two examples into a single numeric value. For instance, consider a shoe data set with only one feature: shoe size. You can quantify how similar two shoes are by calculating the difference between their sizes.

What are similarity algorithms?

Similarity algorithms compute the similarity of pairs of nodes based on their neighborhoods or their properties. Several similarity metrics can be used to compute a similarity score.

What is anomaly detection in cyber security?

What is the difference between GCN and GraphSAGE?

One of the critical difference between GCN and Graphsage is the generalisation of the aggregation function, which was the mean aggregator in GCN. So rather than only taking the average, we use generalised aggregation function in GraphSAGE. GraphSAGE owes its inductivity to its aggregator functions.