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

Abstract: In exemplary implementations of this invention, mobile application (app) installations by users of one or more networks are predicted. Using network data gathered by smartphones, multiple “candidate” graphs (including a call log graph) are calculated. The “candidate” graphs are weighted by an optimization vector and then summed to calculate a composite graph. The composite graph is used to predict the conditional probabilities that the respective users will install an app, depending in part on whether the user's neighbors have previously installed the app. Exogenous factors, such as the app's quality, may be taken into account by creating a virtual candidate graph. The conditional probabilities may be used to select a subset of the users. Signals may be sent to the subset of users, including to recommend an app. Also, the probability of successful “trend ignition” may be predicted from network data.

Abstract: In exemplary implementations of this invention, one or more computer processors receive electronic data indicative of, or compute (i) at least three different topologies of a network and (ii) a level of network performance of a task for each of the different topologies, respectively. The processors also calculate (i) a cascade probability for each of the different topologies, respectively, (ii) a curve indicative of correlation between the cascade probabilities and levels of network performance, and (iii) an optimal cascade probability which optimizes the level of network performance. A topological change in the network is produced (or its likelihood is increased). The topological change makes or would make the cascade probability closer to the optimal cascade probability. The processors output control signals (i) to make the topological change or (ii) to communicate an incentive for the topological change to an electronic node device in the network.

Abstract: In exemplary implementations of this invention, one or more computer processors receive electronic data indicative of, or compute (i) at least three different topologies of a network and (ii) a level of network performance of a task for each of the different topologies, respectively. The processors also calculate (i) a cascade probability for each of the different topologies, respectively, (ii) a curve indicative of correlation between the cascade probabilities and levels of network performance, and (iii) an optimal cascade probability which optimizes the level of network performance. A topological change in the network is produced (or its likelihood is increased). The topological change makes or would make the cascade probability closer to the optimal cascade probability. The processors output control signals (i) to make the topological change or (ii) to communicate an incentive for the topological change to an electronic node device in the network.

Abstract: In exemplary implementations of this invention, mobile application (app) installations by users of one or more networks are predicted. Using network data gathered by smartphones, multiple “candidate” graphs (including a call log graph) are calculated. The “candidate” graphs are weighted by an optimization vector and then summed to calculate a composite graph. The composite graph is used to predict the conditional probabilities that the respective users will install an app, depending in part on whether the user's neighbors have previously installed the app. Exogenous factors, such as the app's quality, may be taken into account by creating a virtual candidate graph. The conditional probabilities may be used to select a subset of the users. Signals may be sent to the subset of users, including to recommend an app. Also, the probability of successful “trend ignition” may be predicted from network data.

Abstract: A system and method in which one or more components from a game may be selected and then automatically detected during game play. The components may optionally be detected automatically according to one or more predefined criteria. Alternatively, the components may optionally be detected through analysis of saved game playing data, such that optionally and more preferably, the extraction process may be performed according to one or more criteria that are set after game play has occurred.

Abstract: A method for job selection and resource allocation of massively parallel processors, the method includes: providing to a constraint satisfaction problem solver multiple domains, variables, and constraints representative of a massively parallel processor, of queued job requests and of jobs being processed by the massively parallel processor, and generating, by the constraint satisfaction problem solver a result representative of multiple jobs to be executed, at least partially in parallel, by multiple resources of the massively parallel processor.

Abstract: A technological platform in which “clips” or short segments of play from a game may be automatically extracted and optionally analyzed, for example to support later searching of such clips for a clip having one or more features of interest.

Abstract: The present invention is a system for using a collective computing power of a plurality of network stations in a communication network in order to overcome threats generated by malicious applications. Collaboratively, a large group of simple network stations implement a vaccination mechanism, proliferating information concerning malicious applications (malwares) throughout the network in an efficient manner.

Abstract: System and method for optimizing TCP window size in operating systems.

Abstract: An efficient method for a reliable and accurate rendering of audio in a virtual reality environment, such as computer gaming. It excels other known rendering methods in its ability to achieve high quality audio rendering in real time conditions using a regular personal computer without any need for a special hardware accelerated audio card. A total of k physical properties are defined for the sound source and for the various objects that may affect the generation and distribution of the sound in the simulated environment. For each one of those physical properties some applicable values are predefined in a granularity which is a parameter of the method. This way a k dimensional grid of data points is defined. For each one of those data point the sounds that will be heard by the listener are offline calculated offline as reference samples.

Abstract: A method and system for providing highlights to game players, including ranking of the highlights, also optionally coaching and other types of interactions between players and also between each player and an external entity.

Abstract: A method for job selection and resource allocation of massively parallel processors, the method includes: providing to a constraint satisfaction problem solver multiple domains, variables, and constraints representative of a massively parallel processor, of queued job requests and of jobs being processed by the massively parallel processor, and generating, by the constraint satisfaction problem solver a result representative of multiple jobs to be executed, at least partially in parallel, by multiple resources of the massively parallel processor.