• Publications
  • Influence
Graph structure in the Web
TLDR
The study of the web as a graph yields valuable insight into web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. Expand
Pig latin: a not-so-foreign language for data processing
TLDR
A new language called Pig Latin is described, designed to fit in a sweet spot between the declarative style of SQL, and the low-level, procedural style of map-reduce, which is an open-source, Apache-incubator project, and available for general use. Expand
Rank aggregation methods for the Web
TLDR
A set of techniques for the rank aggregation problem is developed and compared to that of well-known methods, to design rank aggregation techniques that can be used to combat spam in Web searches. Expand
Image indexing using color correlograms
TLDR
Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval. Expand
Trawling the Web for Emerging Cyber-Communities
TLDR
The subject of this paper is the systematic enumeration of over 100,000 emerging communities from a Web crawl, motivating a graph-theoretic approach to locating such communities, and describing the algorithms and algorithmic engineering necessary to find structures that subscribe to this notion. Expand
Comparing top k lists
TLDR
Besides the applications to the task of identifying good notions of (dis-)similarity between two top k lists, the results imply polynomial-time constant-factor approximation algorithms for the rank aggregation problem with respect to a large class of distance measures. Expand
Influence and correlation in social networks
TLDR
Two simple tests are proposed that can identify influence as a source of social correlation when the time series of user actions is available and are applied to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence. Expand
Microscopic evolution of social networks
TLDR
A complete model of network evolution, where nodes arrive at a prespecified rate and select their lifetimes, and the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases is presented. Expand
Structure and evolution of online social networks
TLDR
A simple model of network growth is presented, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of thenetwork. Expand
The Web as a Graph: Measurements, Models, and Methods
TLDR
This paper describes two algorithms that operate on the Web graph, addressing problems from Web search and automatic community discovery, and proposes a new family of random graph models that point to a rich new sub-field of the study of random graphs, and raises questions about the analysis of graph algorithms on the Internet. Expand
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