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.read more
Citations
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ReportDOI
Cyber Graph Queries for Geographically Distributed Data Centers
TL;DR: New algorithms for a distributed model for graph computations motivated by limited information sharing are presented and some initial attempts to distinguish human nodes from automated nodes in social networks based only on topological properties are described.
Posted Content
Discovering Dense Correlated Subgraphs in Dynamic Networks
TL;DR: In this paper, the authors define the problem as the enumeration of the maximal subgraphs that satisfy specific density and similarity thresholds, and propose an approximate algorithm that scales well with the size of the network.
Journal ArticleDOI
Efficient parallel implementation of the multiplicative weight update method for graph-based linear programs
TL;DR: In this paper , the multiplicative weight update (MWU) algorithm is used to solve graph problems such as densest subgraph, bipartite matching, vertex cover and dominating set.
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MOHCS: Towards Mining Overlapping Highly Connected Subgraphs
TL;DR: This paper first proves some properties related to highly connected graph, then redefine the highly connected subgraph, which results in an algorithm that determines whether a given graph is highly connected in linear time, and presents a computationally efficient algorithm, called MOHCS, for mining overlapping highlyconnected subgraphs.
Journal ArticleDOI
Discovering top-weighted k-truss communities in large graphs
TL;DR: In this paper , the authors consider both link structure and link weight to discover top-r weighted k-truss communities via community search, which are those communities with the highest weight and the highest cohesiveness within the network.
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.