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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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Posted Content

Connected-Dense-Connected Subgraphs in Triple Networks.

TL;DR: The problem of finding ConnectedDense-Connected subgraph (CDC), a subnetwork which has the largest density in the bipartite network and whose sets of end points within each network induce connected subnetworks, is investigated and novel heuristics are developed to obtain feasible solutions.
Posted Content

The Wedge Picking Model: A dynamic graph model based on triadic closure.

TL;DR: A probabilistic mechanism to model the evolution of dynamic graphs, inspired by the concept of triadic closure, is proposed and a scheduling subroutine is developed to process modifications of the graph in batches.
Journal ArticleDOI

Related Work on CSMs and Solutions

TL;DR: In this paper , the authors thoroughly review the five groups of works on CSS on homogeneous networks, which are core-, truss-, clique-, connectivity-, and density-based CSMs and solutions, and also review the works on HIN clustering and compare it with the earlier version of this book.
Posted Content

Top-k densest subgraphs in sliding-window graph streams.

TL;DR: This paper studies the top-k densest subgraph problem in the sliding-window model and proposes an efficient fully-dynamic algorithm that profits from the observation that updates only affect a limited region of the graph.
DissertationDOI

Extracting large quasi-bicliques using a skeleton-based heuristic

Nick Pappas
TL;DR: This chapter discusses the motivation for the skeleton based heuristic, which is based on weighted quasi bicliques and its applications in the context of an ILP web application.
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.