Book ChapterDOI
Greedy approximation algorithms for finding dense components in a graph
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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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Proceedings ArticleDOI
Distance-generalized Core Decomposition
TL;DR: This work introduces a distance-based generalization of the notion of k-core, which it is shown that it preserves many of the nice features of the classic core decomposition and preserves its usefulness to speed-up or approximate distance-generalized notions of dense structures, such as h-club.
Proceedings ArticleDOI
Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings
TL;DR: This paper proposes a novel Coupled Unsupervised Feature Selection framework (CUFS for short) to filter out noisy or redundant features for subsequent outlier detection in categorical data and proves that DSFS retains a 2-approximation feature subset to the optimal subset.
Journal ArticleDOI
Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization
Bryan Perozzi,Leman Akoglu +1 more
TL;DR: A new measure of subgraph quality for attributed communities called normality, a community extraction algorithm that uses normality to extract communities and a few characterizing attributes per community, and a summarization and interactive visualization approach for attributed graph exploration are introduced.
Book ChapterDOI
HiDDen: Hierarchical dense subgraph detection with application to financial fraud detection
Si Zhang,Dawei Zhou,Mehmet Yigit Yildirim,Scott Alcorn,Jingrui He,Hasan Davulcu,Hanghang Tong +6 more
TL;DR: The key idea of the method is to envision the density of subgraphs as a relative measure to its background (i.e., the subgraph at the coarse granularity) to solve the hierarchical dense subgraph detection problem.
Book ChapterDOI
Link prediction for annotation graphs using graph summarization
Andreas Thor,Philip Anderson,Louiqa Raschid,Saket Navlakha,Barna Saha,Samir Khuller,Xiao-Ning Zhang +6 more
TL;DR: A novel approach for link prediction is proposed; it is a preliminary task when discovering more complex patterns in annotation graph datasets and is based on a complementary methodology of graph summarization and dense subgraphs (DSG).
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.