Size-Estimation Framework with Applications to Transitive Closure and Reachability
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TLDR
This work presents anO(m) time randomized (Monte Carlo) algorithm that estimates, with small relative error, the sizes of all reachability sets and the transitive closure.About:
This article is published in Journal of Computer and System Sciences.The article was published on 1997-12-01 and is currently open access. It has received 448 citations till now. The article focuses on the topics: Transitive reduction & Transitive closure.read more
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Proceedings ArticleDOI
Efficient influence maximization in social networks
Wei Chen,Yajun Wang,Siyu Yang +2 more
TL;DR: Based on the results, it is believed that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time.
Proceedings ArticleDOI
Google news personalization: scalable online collaborative filtering
TL;DR: This paper describes the approach to collaborative filtering for generating personalized recommendations for users of Google News using MinHash clustering, Probabilistic Latent Semantic Indexing, and covisitation counts, and combines recommendations from different algorithms using a linear model.
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Ligra: a lightweight graph processing framework for shared memory
Julian Shun,Guy E. Blelloch +1 more
TL;DR: This paper presents a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write and significantly more efficient than previously reported results using graph frameworks on machines with many more cores.
Proceedings ArticleDOI
Influence Maximization in Near-Linear Time: A Martingale Approach
TL;DR: The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
References
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Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book
The Art of Computer Programming
TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Journal ArticleDOI
An Introduction to Probability Theory and Its Applications
David A. Freedman,William Feller +1 more