Microsoft Technology Licensing, LLC

Rovi Technologies Corporation

Jennifer Tour Chayes

Christian Herwarth Borgs

Seyed Vahab Mirrokni

John E Hopcroft

Mohammad Mahdian

Dimitris Achlioptas

Henry J Robinson

Krishna Chaitanya Gade

Amin Saberi

James R Tierney

Amit Prakash

Christian L Belady

Navendu Jain

James R Larus

Danny Allen Reed

Ishai Menache

Reid Andersen

Tao Tao

Moshe Tennenholtz

Adam Tauman Kalai

Ilan Lobel

Hamid Nazerzadeh

Shang-Hua Teng

Uriel Mordechai Feige

Christopher A Meek

David Maxwell Chickering

Kamal Jain

Abraham D Flaxman

Nicole S Immorlica

Vahab Mirrokni

Brendan James Lucier

Azarakhsh Malekian

Bela S Bollobas

Maria Florina Balcan

Oliver Maxim Riordan

Mark Braverman

Shanghua Teng

Reid M Anderson

Mohsen Bayati

Alfredo Braunstein

Michael Avraham Brautbar

Riccardo Zecchina

A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a 'stripped down' architecture to complement its use in the above-summarized data-centric environment.

Data center system that accommodates episodic computation

An influence maximization process efficiently identifies an influential set of nodes with which to seed a diffusion process using the transposition of a graph representing the network. This approach offers an acceptable tradeoff between runtime complexity and accurate approximation. In addition, using an approximation condition, the influence maximization process may be further tuned to dramatically reduce the computational complexity even more in certain circumstances while allowing a fallback to the unturned influence maximization process if the approximation condition is not satisfied.

Determining influence in a network

A recommendation ranking system that computes trust for entities based on negative expressions of trust. Negative expressions of trust are used to reduce the trust of entities. However, the system may discount entities that in the aggregate are distrusted. The system may be used with a social network to provide accurate, personalized recommendations for members of the social network. The network may be modeled as a voting network, with each member of the social network represented as a node and expressions of trust between members represented as weights on edges between nodes. Values of trust may be computed for nodes in the network and used to generate a recommendation. Opinions expressed on a topic may be weighted by trust in the node expressing the opinion. The system may be applied in other settings that can be modeled as a voting network, including ranking of Internet search results.

Recommendation ranking system with distrust

A technique for identifying overlapping clusters of items in a data set. The technique may be used in connection with a social network or other on-line environment in which users express approval for other users, such as through votes, tags or other inputs. These expressions of approval may be used to form clusters such that entities assigned to a cluster have a higher metric of approval from other entities within the cluster than from outside the cluster. Such clusters may be arrived at through a computationally efficient approach that involves randomly selecting one or more entities as a seed for a cluster. The cluster may be grown by testing other entities, similar to those already in the cluster, to determine whether they are more preferred by those already in the cluster than those outside the cluster. Once a cluster is grown to a desired size, it may be pruned.

Detecting overlapping clusters

A SPAM detection system is provided. The system includes a graph clustering component to analyze web data. A link analysis component can be associated with the graph clustering component to facilitate SPAM detection in accordance with the web data.

Spam score propagation for web spam detection

The claimed subject matter relates to an architecture that can identify, store, and/or output local contributions to a rank of a vertex in a directed graph. The architecture can receive a directed graph and a parameter, and examine a local subset of vertices (e.g., local to a given vertex) in order to determine a local supporting set. The local supporting set can include a local set of vertices that each contributes a minimum fraction of the parameter to a rank of the vertex. The local supporting set can be the basis for an estimate of the supporting set and/or rank of the vertex for the entire graph and can be employed as a means for detecting link or web spam as well as other influence-based social network applications.

Local computation of rank contributions

A system and/or method that facilitates analyzing newsgroup clusters. A data reception component receives data relating to a plurality of newsgroups and relays the data to an engine that constructs a weighted graph. The weighted graph represents a subset of the newsgroups as vertices of the graph. The vertices are connected by edges, which represent cross-postings relating to the subset of newsgroups.

News group clustering based on cross-post graph

The claimed subject matter provides a system and/or a method that facilitates reducing spam in search results. An interface can obtain web graph information that represents a web of pages. A spam detection component can determines one or more features based at least in part on the web graph information. The one or more features can provide indications that a particular page of the web graph is spam. In addition, a robust rank component is provided that limits amount of contribution a single page can provide to the target page.

Locally computable spam detection features and robust pagerank

Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.

Trust-based recommendation systems

Neighborhood clustering for web spam detection

Networks may be analyzed using Steiner trees. In an example embodiment, a method includes acts of receiving, accepting, creating, and analyzing. Data specifying a network is received. Steiner tree parameters are accepted. A Steiner tree model is created on the data specifying the network responsive to the Steiner tree parameters. The Steiner tree model includes a local representation having a depth constraint for neighborhood vertices. The data specifying the network is analyzed to ascertain a Steiner tree solution based on the Steiner tree model, which includes the local representation, and using a locally-oriented combinatorial algorithm.

Network analysis with Steiner trees

Systems and methods for generating models of directed scale-free object relationships are described. In one aspect, a sequence of random numbers is generated. Individual ones of these random numbers are then selected over time to generate the directed scale-free object relationships as a graph based on sequences of in-degrees and out-degrees.

Modeling directed scale-free object relationships

The subject invention relates to systems and methods that provide a truthful incentive-compatible mechanism for budget-constrained bidders in order to facilitate asymptotically maximizing revenue with multi-unit auctions in which individual bidders have private valuation per item (e.g., bids, offer prices, utility . . . ) and overall budgets. The systems and/or methods obtain bidder private bids and budget constraint, and separate this information into groups of bids and budgets. Each group is randomly reordered and utilized to determine a profit maximizing price as a function of the bids and budgets within that group. The groups of bids and budgets and the profit maximizing prices are utilized to allocate the auctioned items to respective bidders such that the allocation is based at least in part on the bids, budgets and prices. The systems and methods can be utilized to improve online auctioning.

Systems and methods that facilitate maximizing revenue for multi-unit auctions with private budgets

A computer-implemented system that facilitates establishing price(s) associated with items comprises a clustering component that clusters a collection of non-identical items into one or more sets of non-identical items. A pricing component receives one or more of active bids and stored bids and simulates an auction of at least one non-identical item within one of the one or more sets of non-identical items as if the non-identical items within the set were identical to determine price(s) associated with the at least one non-identical item. For example, the items can be at least portions of search terms received by a search engine.

Price determination for items of low demand

Growing community of inventors

Seattle, WA, United States of America

Jennifer Tour Chayes