Abstract: There is provided a method for evaluating a network comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, the dynamic graph denoting the network, computing sets of meta-parameters, each set of meta-parameters computed according to a respective graph of the graphs, wherein each one of the meta-parameters denotes a network level parameter computed according to a plurality of at least one of edges and nodes of the respective graphs, analyzing sets of meta-parameters according to values computed based on a physics-based analytical model of an evolving physical system, and predicting a likelihood of stabilization of the network during a future time interval according to an indication of convergence of the values according to a convergence requirement, computed based on the physics-based analytical model during the future time interval.

Abstract: There is provided a method for adapting components of a network, comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, wherein nodes of the graphs denote entities, and edges of the graphs denote interactions between the entities over a network, computing community graphs according to the graphs, computing meta-community graphs according to the community graphs, analyzing dynamics of the community graphs to detect changes between two temporally adjacent community graphs, analyzing dynamics of the meta-community graphs to detect changes between two temporally adjacent meta-community graphs, identifying at least one entity corresponding to node(s) of the dynamic graph according to a predicted likelihood of performing an anomalous action during a future time interval, and generating instructions for adapting component(s) of the network for ensuring availability of network resources for interactions between entities during the

Abstract: There is provided a method for evaluating a network comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, the dynamic graph denoting the network, computing sets of meta-parameters, each set of meta-parameters computed according to a respective graph of the graphs, wherein each one of the meta-parameters denotes a network level parameter computed according to a plurality of at least one of edges and nodes of the respective graphs, analyzing sets of meta-parameters according to values computed based on a physics-based analytical model of an evolving physical system, and predicting a likelihood of stabilization of the network during a future time interval according to an indication of convergence of the values according to a convergence requirement, computed based on the physics-based analytical model during the future time interval.

Abstract: There is provided a method for adapting components of a network, comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, wherein nodes of the graphs denote entities, and edges of the graphs denote interactions between the entities over a network, computing community graphs according to the graphs, computing meta-community graphs according to the community graphs, analyzing dynamics of the community graphs to detect changes between two temporally adjacent community graphs, analyzing dynamics of the meta-community graphs to detect changes between two temporally adjacent meta-community graphs, identifying at least one entity corresponding to node(s) of the dynamic graph according to a predicted likelihood of performing an anomalous action during a future time interval, and generating instructions for adapting component(s) of the network for ensuring availability of network resources for interactions between entities during the

Abstract: There is provided a method for computing an encrypted prediction in response to an encrypted query, comprising: obtaining an encrypted dataset comprising encrypted records for respective encrypted entities, each record storing encrypted parameter values of parameters and an associated indication of the respective entity, computing abnormality clusters according to the records of the encrypted dataset, wherein each of the abnormality clusters stores indications of entities of records of the encrypted dataset having mathematically significant common abnormal feature(s) that statistically differentiates records of the respective abnormality cluster from other records of the encrypted dataset, receiving a query comprising target indications of respective target entities associated with common feature(s), and analyzing the query according to the abnormality clusters to identify at least one encrypted result entity indication according to a likelihood of the encrypted result entity indication predicted to correlate

Abstract: There is provided a method for adapting components of a network, comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, wherein nodes of the graphs denote entities, and edges of the graphs denote interactions between the entities over a network, computing community graphs according to the graphs, computing meta-community graphs according to the community graphs, analyzing dynamics of the community graphs to detect changes between two temporally adjacent community graphs, analyzing dynamics of the meta-community graphs to detect changes between two temporally adjacent meta-community graphs, identifying at least one entity corresponding to node(s) of the dynamic graph according to a predicted likelihood of performing an anomalous action during a future time interval, and generating instructions for adapting component(s) of the network for ensuring availability of network resources for interactions between entities during the

Abstract: There is provided a method for computing an encrypted prediction in response to an encrypted query, comprising: obtaining an encrypted dataset comprising encrypted records for respective encrypted entities, each record storing encrypted parameter values of parameters and an associated indication of the respective entity, computing abnormality clusters according to the records of the encrypted dataset, wherein each of the abnormality clusters stores indications of entities of records of the encrypted dataset having mathematically significant common abnormal feature(s) that statistically differentiates records of the respective abnormality cluster from other records of the encrypted dataset, receiving a query comprising target indications of respective target entities associated with common feature(s), and analyzing the query according to the abnormality clusters to identify at least one encrypted result entity indication according to a likelihood of the encrypted result entity indication predicted to correlate