11776735
Query incentive networks
The concurrent growth of on-line communities exhibiting large-scale social structure, and of large decentralized peer-to-peer file-sharing systems, has stimulated new interest in understanding networks of interacting agents as economic systems. Here we formulate a model for query incentive networks, motivated by such systems: users seeking information or services can pose queries, together with incentives for answering them, that are propagated along paths in a network. This type of information-seeking process can be formulated as a game among the nodes in the network, and this game has a natural Nash equilibrium. In such systems, it is a fundamental question to understand how much incentive is needed in order for a node to achieve a reasonable probability of obtaining an answer to a query from the network. We study the size of query incentives as a function both of the rarity of the answer and the structure of the underlying network. This leads to natural questions related to strategic behavior in branching processes. Whereas the classically studied criticality of branching processes is centered around the region where the branching parameter is 1, we show in contrast that strategic interaction in incentive propagation exhibits critical behavior when the branching parameter is 2.
43859978
Encoding XML in Vector Spaces
We develop a framework for representing XML documents and queries in vector spaces and build indexes for processing text-centric semi-structured queries that support a proximity measure between XML documents. The idea of using vector spaces for XML retrieval is not new. In this paper we (i) unify prior approaches into a single framework; (ii) develop techniques to eliminate special purpose auxiliary computations (outside the vector space) used previously; (iii) give experimental evidence on benchmark queries that our approach is competitive in its retrieval quality and (iv) as an immediate consequence of the framework, are able to classify and cluster XML documents.
34246581
Query Incentive Networks
We formulate a model for query incentive networks, motivated by users seeking information or services that pose queries, together with incentives for answering them. This type of information-seeking process can be formulated as a game among the nodes in the network, and this game has a natural Nash equilibrium. 
 
How much incentive is needed in order to achieve a reasonable probability of obtaining an answer to a query? We study the size of query incentives as a function both of the rarity of the answer and the structure of the underlying network. This leads to natural questions related to strategic behavior in branching processes. Whereas the classically studied criticality of branching processes is centered around the region where the branching parameter is 1, we show in contrast that strategic interaction in incentive propagation exhibits critical behavior when the branching parameter is 2. 
 
This lecture is based on the paper [14] with Jon Kleinberg of Cornell University.
8030181
Finding near neighbors through cluster pruning
Finding near(est) neighbors is a classic, difficult problem in data management and retrieval, with applications in text and image search,in finding similar objects and matching patterns. Here we study cluster pruning, an extremely simple randomized technique. During preprocessing we randomly choose a subset of data points to be leaders the remaining data points are partitioned by which leader is the closest. For query processing, we find the leader(s) closest to the query point. We then seek the nearest neighbors for the query point among only the points in the clusters of the closest leader(s). Recursion may be used in both preprocessing and in search. Such schemes seek approximate nearest neighbors that are "almost as good" as the nearest neighbors. How good are these approximations and how much do they save in computation.
 Our contributions are: (1) we quantify metrics that allow us to study the tradeoff between processing and the quality of the approximate nearest neighbors; (2) we give rigorous theoretical analysis of our schemes, under natural generative processes (generalizing Gaussian mixtures) for the data points; (3) experiments on both synthetic data from such generative processes, as well as on from a document corpus, confirming that we save orders of magnitude in query processing cost at modest compromises in the quality of retrieved points. In particular, we show that p-spheres, a state-of-the-art solution, is outperformed by our simple scheme whether the data points are stored in main or in external memo.
13408394
Structure and evolution of blogspace
A critical look at more than one million bloggers and the individual entries of some 25,000 blogs reveals blogger demographics, friendships, and activity patterns over time.
15130102
Towards a Grid enabled system for multicomponent materials design
We are developing a portal for multicomponent materials design using Grid-enabled large-scale simulations. In this paper we report on our services based application architecture which allows integration of simulation software with Grid services to provide a Web-based computational laboratory for the modeling of Al-Cu-Mg-Si alloys. We examine user requirements and describe the design of our framework. Our architecture is implemented using existing middleware such as the Globus and Java CoG toolkits. An interesting feature of our design is the separation of the high-level specification of the materials modeling system from the implementation through the use of a markup language such as XML. We use markup languages with domain-specific extensions to specify (in architecture-independent form) rules and constraints that allow meaningful composition of simulation tasks and experimentally determined material properties. In addition, we use them to specify application code interfaces so that our simulation server can be dynamically reconfigured to include new software and constraints.