Google Inc.

Amogh S Asgekar

Madhukar N Thakur

Kiran Singh Panesar

Ranveer Kunal

Sandesh Tawari

Kiran S Panesar

Kumara Tharmalingam

Piyush Prahladka

Finnegan Southey

A digital content acceleration system comprising: a keyed database for storing keyed data; a data retrieval engine that retrieves, in response to receiving an item of keyed data, one of i) search data indicative of a search history associated with the item of keyed data, ii) web history data indicative of one or more web pages accessed by a terminal device associated with the item of keyed data, or iii) both i) and ii); a parsing engine that extracts one or more attributes from the search data and the web history data; a terminal device network access engine that generates, based on the one or more attributes, a probability distribution for geographic locations; a content selection engine that retrieves, from a digital content provider, digital content associated with a particular geographic location of the geographic locations; and a digital content assembler engine that pre-assembles the digital content.

Search and retrieval of keyed data maintained using a keyed database

In one implementation, a computer-implemented method includes receiving information related to users of a social network site and determining a graph for at least a portion of the social network, the graph including a plurality of nodes connected by directional links where each node corresponds to a user having a profile page on the social network and where each directional link corresponds to a user viewing at least an aspect of a profile page for another user. The method can further include identifying first nodes as including content associated with a particular subject of interest, and seeding the identified first nodes with first scores. The method can also include determining second scores for second nodes by propagating the first scores across the social graph based upon third nodes having viewed aspects of profile pages associated with the first nodes and the second nodes; and providing the determined second scores.

Detecting content on a social network using browsing patterns

Users in a social network are represented by nodes on a network graph. A similarity processor generates a similarity matrix of nodes and neighbors. A clustering processor groups select nodes based on similarity. Nodes initially assigned to one cluster are selectively added to other clusters based on similarity. A social network processor provides features and processing based on the clusters of nodes thus produced.

Social network node clustering system and method

A method for categorizing multiple users of a website based on their respective transition patterns between successive types of user activities is implemented at a computing device having one or more processors and memory. After receiving one or more log files from a data source, the computing device generates respective event transition matrixes for multiple users in accordance with the users' event records such that each element of each respective event transition matrix includes a probability of an occurrence of two consecutive event types A and B for the same user. Based on an analysis of the multiple users' event transition matrixes, the computing device categorizes the multiple users into at least two distinct groups of users and performs one or more operations to the website's user accounts associated with one or more users from one of the groups.

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Navi Mumbai, India

Amogh S Asgekar