Software Technology for Methods and systems for predicting anomalies

Netz Forecasts Ltd. (Tel-Aviv, IL)

The method is described as a way for adapting elements of a network, comprising: providing graphs each representing a particular sequential snapshot of a dynamic graph obtained over the course of a time period that is historical wherein the nodes of the graphs denote entities, while edges of graphs represent interactions between the entities over the network, computing community graphs based on graphs and computing meta-community graphs in accordance to the community graphs analysing the dynamics of community graphs to detect changes between two temporally adjacent community graphs. meta-community graphs’ dynamics to identify the changes between two adjacent temporally meta-community graphs, identifying at least one person which is related to node(s) of the dynamic graph based on the likelihood that it is likely to perform an unnatural action during a future time interval, and generating instructions to modify the component(s) of the network to ensure availability of network resources to support interactions between entities in the future time interval.

The present invention, as various embodiments of it, relates to the detection of anomalies, and more specifically, but certainly not exclusively to detecting abnormal actions of the nodes in a network.

Anomalous actions within the computer network could be the result of data that does not conform to a pattern that is expected. Anomalous actions may be indicative of a problem, or may be indicative of unexpected actions that are not necessarilyproblematic or malicious.

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