NDLI: Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs

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Creating user interfaces that entice people to manage better information

Data, health, and algorithmics: computational challenges for biomedicine

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Unsupervised transactional query classification based on webpage form understanding

Suggestion set utility maximization using session logs

Discovering missing click-through query language information for web search

Learning to aggregate vertical results into web search results

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Simulating simple user behavior for system effectiveness evaluation

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Bayesian latent variable models for collaborative item rating prediction

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Harvesting facts from textual web sources by constrained label propagation

Cloning for privacy protection in multiple independent data publications

Can irrelevant data help semi-supervised learning, why and how?

Discovering top-k teams of experts with/without a leader in social networks

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Scalable density-based subspace clustering

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Towards feature selection in network

Plagiarism detection based on structural information

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Finding dimensions for queries

Simultaneous joint and conditional modeling of documents tagged from two perspectives

Automated feature generation from structured knowledge

Estimating selectivity for joined RDF triple patterns

Semi-indexing semi-structured data in tiny space

Learning-based relevance feedback for web-based relation completion

Multiple keyword-based queries over XML streams

I/O-efficient algorithms for answering pattern-based aggregate queries in a sequence OLAP system

Ranking support for keyword search on structured data using relevance models

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Learning to target: what works for behavioral targeting

Enriching textbooks with images

Search result diversification for enterprise data

Spectral analysis of a blogosphere

Scalable entity matching computation with materialization

Exploratory search over social-medical data

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Coreference aware web object retrieval

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Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs

Content Provider	ACM Digital Library
Author	García-Bañuelos, Luciano Dumas, Marlon Armas-Cervantes, Abel Vilo, Jaak Tretyakov, Konstantin
Abstract	Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of approximate methods have been proposed, including several landmark-based methods that have been shown to scale up to very large graphs with acceptable accuracy. This paper presents two improvements to existing landmark-based shortest path estimation methods. The first improvement relates to the use of shortest-path trees (SPTs). Together with appropriate short-cutting heuristics, the use of SPTs allows to achieve higher accuracy with acceptable time and memory overhead. Furthermore, SPTs can be maintained incrementally under edge insertions and deletions, which allows for a fully-dynamic algorithm. The second improvement is a new landmark selection strategy that seeks to maximize the coverage of all shortest paths by the selected landmarks. The improved method is evaluated on the DBLP, Orkut, Twitter and Skype social networks.
Starting Page	1785
Ending Page	1794
Page Count	10
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063834
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trees Dynamic updates Landmarks Social networks Graph databases Shortest paths
Content Type	Text
Resource Type	Article

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