Abstract: A Reasoning Graph is defined as a directed graph characterized by nodes that can be, but are not limited to, concepts, situations, conditions, events, and persons with edges between nodes if one node is a cause of another node (its effect). The Reasoning Graph is automatically generated by analyzing a large corpus text from a repository such as the Wide World Web and other sources. The analysis leads to creation of edges within the graph based on, for example, lexical and semantic analysis of the text. Nodes of the Reasoning Graph can be ranked according to eigenvalue analysis of the connectivity matrix associated with the Reasoning Graph, including through application of both random as well as reasoning surfer models.

Abstract: A Reasoning Graph is defined as a directed graph characterized by nodes that can be, but are not limited to, concepts, situations, conditions, events, and persons with edges between nodes if one node is a cause of another node (its effect). The Reasoning Graph is automatically generated by analyzing a large corpus text from a repository such as the Wide World Web and other sources. The analysis leads to creation of edges within the graph based on, for example, lexical and semantic analysis of the text. Nodes of the Reasoning Graph can be ranked according to eigenvalue analysis of the connectivity matrix associated with the Reasoning Graph, including through application of both random as well as reasoning surfer models.

Abstract: Systems and methods in which a ranking engine, for example such an engine as is communicatively coupled to a search engine or social networking system, or is part of such a system, ranks social offerings for eventual display to a user, said social offerings being ranked according to eigenvalues of matrices associated with a social graph G of the user and the user's friends and other connections. In some instances, the social offerings may be divided into categories and ranks for the social offerings computed for each of the categories based on activities and communications of the user with friends and other connections for each category. The eigenvalues are used for ranking the social offerings based on actions of the user's friends and other connections with respect to the social offerings.

Abstract: Systems and methods in which a ranking engine, for example such an engine as is communicatively coupled to a search engine or social networking system, or is part of such a system, ranks social offerings for eventual display to a user, said social offerings being ranked according to eigenvalues of matrices associated with a social graph G of the user and the user's friends and other connections. In some instances, the social offerings may be divided into categories and ranks for the social offerings computed for each of the categories based on activities and communications of the user with friends and other connections for each category. The eigenvalues are used for ranking the social offerings based on actions of the user's friends and other connections with respect to the social offerings.

Abstract: Members of a social network user's social graph are automatically segregated into overlapping clusters according to patterns of their past communications. Each cluster within the social graph represents a group of members having a high degree of intra-cluster communication or other connection with one another. The clustering is performed according to a sorting or ranking in accordance with non-principal eigenvectors of connectivity matrices describing the intra-cluster communications/connections. The overlapping clusters exhibit maximum internal density and minimum external sparsity.

Abstract: Members of a social network user's social graph are automatically segregated into overlapping clusters according to patterns of their past communications. Each cluster within the social graph represents a group of members having a high degree of intra-cluster communication or other connection with one another. The clustering is performed according to a sorting or ranking in accordance with non-principal eigenvectors of connectivity matrices describing the intra-cluster communications/connections. The overlapping clusters exhibit maximum internal density and minimum external sparsity.

Abstract: Content items in a distributed system are defined by a respective key, and each such content item is copied to R1 computer systems of the distributed system which have unique identifiers closest to a value of the respective key, where R1 is less than R2 which is less than R3, at least R2 number of the computer systems have copies of any respective one of the content items for all of the content items, and none of the computer systems farther than R3 in an address space of the distributed system have a copy of the subject content item. Modifications of individual content items are synchronized across all instances thereof responsive to a put operation and/or at periodic intervals.

Abstract: Linked documents are ranked by observing link selections for referred documents from referring documents and counting such selections. The counts for each of the link selections are stored at various computer systems of a distributed network, a centralized collection of computers connected through a local network, or a hybrid system (collectively, the “system”) consisting of combinations of distributed and centralized systems, and processed (e.g., using a discrete probability distribution defined by the counts of the link selections) to obtain page ranks for the referred documents. The link selections may be observed by a browser extension running on individual ones of the computer systems of the distributed network and the counts of the link selections may be stored at locations within the system determined by a distributed hash table. Search request results may be displayed in a ranked order as determined by the page ranks.

Abstract: Content items in a distributed system are defined by a respective key, and each such content item is copied to R1 computer systems of the distributed system which have unique identifiers closest to a value of the respective key, where R1 is less than R2 which is less than R3, at least R2 number of the computer systems have copies of any respective one of the content items for all of the content items, and none of the computer systems farther than R3 in an address space of the distributed system have a copy of the subject content item. Modifications of individual content items are synchronized across all instances thereof responsive to a put operation and/or at periodic intervals.

Abstract: Linked documents are ranked by observing link selections for referred documents from referring documents and counting such selections. The counts for each of the link selections are stored at various computer systems of a distributed network, a centralized collection of computers connected through a local network, or a hybrid system (collectively, the “system”) consisting of combinations of distributed and centralized systems, and processed (e.g., using a discrete probability distribution defined by the counts of the link selections) to obtain page ranks for the referred documents. The link selections may be observed by a browser extension running on individual ones of the computer systems of the distributed network and the counts of the link selections may be stored at locations within the system determined by a distributed hash table. Search request results may be displayed in a ranked order as determined by the page ranks.

Abstract: Linked documents are ranked by observing link selections for referred documents from referring documents and counting such selections. The counts for each of the link selections are stored at various computer systems of a distributed network, a centralized collection of computers connected through a local network, or a hybrid system (collectively, the “system”) consisting of combinations of distributed and centralized systems, and processed (e.g., using a discrete probability distribution defined by the counts of the link selections) to obtain page ranks for the referred documents. The link selections may be observed by a browser extension running on individual ones of the computer systems of the distributed network and the counts of the link selections may be stored at locations within the system determined by a distributed hash table. Search request results may be displayed in a ranked order as determined by the page ranks.

Abstract: Content items in a distributed system are defined by a respective key, and each such content item is copied to R1 computer systems of the distributed system which have unique identifiers closest to a value of the respective key, where R1 is less than R2 which is less than R3, at least R2 number of the computer systems have copies of any respective one of the content items for all of the content items, and none of the computer systems farther than R3 in an address space of the distributed system have a copy of the subject content item. Modifications of individual content items are synchronized across all instances thereof responsive to a put operation and/or at periodic intervals.

Abstract: Linked documents are ranked by observing link selections for referred documents from referring documents and counting such selections. The counts for each of the link selections are stored at various computer systems of a distributed network, a centralized collection of computers connected through a local network, or a hybrid system (collectively, the “system”) consisting of combinations of distributed and centralized systems, and processed (e.g., using a discrete probability distribution defined by the counts of the link selections) to obtain page ranks for the referred documents. The link selections may be observed by a browser extension running on individual ones of the computer systems of the distributed network and the counts of the link selections may be stored at locations within the system determined by a distributed hash table. Search request results may be displayed in a ranked order as determined by the page ranks.

Abstract: Preferred embodiments of the present invention comprise a system and method for enabling a novice computer user automatically to generate a customized search agent for conducting a content search on one or more remote network Web sites and aggregating the search results. The user has the ability to specify the Web sites that will be searched. The customized search agent then functions to aggregate content found on each of the sites the computer user specifies for the search. In a preferred embodiment of the present invention, the novice user employs a remote inclusion agent automatically to generate the search agent simply by using a standard network browser to interface the inclusion agent and surf Web sites that provide the desired content.

Abstract: A high-speed block id encoder circuit using dynamic logic includes a plurality of input signal lines received from a memory array and a plurality of output signal lines. A first portion of the encoder circuit pre-charges the plurality of output signal lines to a first state. A plurality of transistors coupled together in a single level receives the input signals and discharges the appropriate output signal lines to a second state based on the input signals. The signals produced on the output lines provide an encoded output identifying which one of the plurality of input signal lines is asserted.