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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Journal ArticleDOI
Finding Dense Components in Large-Scale Network Using Randomized Binary Search Tree
TL;DR: This work has reduced its time complexity by using a randomized binary search tree, also called treap, and makes the complexity analysis in both time and memory requirements, including computational experiments in large scale real networks.
Journal ArticleDOI
Convergence to Lexicographically Optimal Base in a (Contra)Polymatroid and Applications to Densest Subgraph and Tree Packing
TL;DR: In this paper , a convergence proof for the super-modular density problem was provided via a connection to the quadratic program for finding a lexicographically optimal base in a (contra) polymatroid.
Journal ArticleDOI
A memetic algorithm for finding multiple subgraphs that optimally cover an input network
TL;DR: In this paper , a variant of the densest subgraph problem is discussed and a mathematical model for optimizing the total coverage of an input network by extracting multiple subgraphs is proposed and shown to be both effective and efficient.
Proceedings ArticleDOI
Efficient Event Stream Dissemination in Online Social Networks Based on Community Detection
TL;DR: This work proposes a novel scheme by exploiting the social community for event stream dissemination by fully exploiting the proposed hub- structure, based on the observation of high cluster coefficient in OSNs.
Journal ArticleDOI
Efficient Densest Subgraphs Discovery in Large Dynamic Graphs by Greedy Approximation
TL;DR: In this paper , an incremental greedy approximation approach is proposed, and its running time is O(m+n) where n is the number of nodes in the graph and m is the size of the graph.
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.