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Book ChapterDOI

Greedy approximation algorithms for finding dense components in a graph

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TLDR
This paper gives simple greedy approximation algorithms for these optimization problems of finding subgraphs maximizing these notions of density for undirected and directed graphs and answers an open question about the complexity of the optimization problem for directed graphs.
Abstract
We study the problem of finding highly connected subgraphs of undirected and directed graphs. For undirected graphs, the notion of density of a subgraph we use is the average degree of the subgraph. For directed graphs, a corresponding notion of density was introduced recently by Kannan and Vinay. This is designed to quantify highly connectedness of substructures in a sparse directed graph such as the web graph. We study the optimization problems of finding subgraphs maximizing these notions of density for undirected and directed graphs. This paper gives simple greedy approximation algorithms for these optimization problems. We also answer an open question about the complexity of the optimization problem for directed graphs.

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Storage, processing and analysis of large evolving graphs

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Proceedings ArticleDOI

Finding Faces in News Photos Using Both Face and Name Information

TL;DR: A graph-based method is proposed to find the most similar subset among the set of possible faces associated with the query name, where the mostsimilar subset is likely to correspond to the faces of the queried person.
Journal ArticleDOI

Efficient Extraction of Target Users for Package Promotion in Big Social Networks

TL;DR: It is proved that the proposed PGI problem is NP-hard, and a polynomial-time algorithm named incremental solution construction with redundancy and infeasibility avoidance for PGI (ISCP) that can effectively and efficiently obtain a good solution to the P GI problem is developed.
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BadLink: Combining Graph and Information-Theoretical Features for Online Fraud Group Detection

TL;DR: This work builds an extensible fraud detection framework, Badlink, to support multimodal datasets with different data types and distributions in a scalable way and demonstrates the state-of-the-art performance of BadLink, even with sophisticated camouflage traffic.
Posted ContentDOI

A multivariate to multivariate approach for voxel-wise genome-wide association analysis

TL;DR: Zhang et al. as mentioned in this paper proposed a bi-clique graph structure (i.e., a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern.
References
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Proceedings ArticleDOI

Authoritative sources in a hyperlinked environment

TL;DR: This work proposes and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of \hub pages that join them together in the link structure, that has connections to the eigenvectors of certain matrices associated with the link graph.
Journal ArticleDOI

Trawling the Web for emerging cyber-communities

TL;DR: 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.
Book ChapterDOI

The web as a graph: measurements, models, and methods

TL;DR: 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.
Proceedings ArticleDOI

Inferring Web communities from link topology

TL;DR: This investigation shows that although the process by which users of the Web create pages and links is very difficult to understand at a “local” level, it results in a much greater degree of orderly high-level structure than has typically been assumed.
Trending Questions (1)
Calculate the density of directed and undirected graph?

The density of a directed graph is defined as the maximum density of any subset of vertices, while the density of an undirected graph is defined as the maximum density of any subset of vertices.