Distributed k-Core Decomposition
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TLDR
New distributed algorithms for the computation of the k- coreness of a network, a process also known as k-core decomposition, are proposed and an exhaustive experimental analysis on real-world data sets is provided.Abstract:
Several novel metrics have been proposed in recent literature in order to study the relative importance of nodes in complex networks. Among those, k-coreness has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-coreness of a network, a process also known as k-core decomposition. This technique 1) allows the decomposition, over a set of connected machines, of very large graphs, when size does not allow storing and processing them on a single host, and 2) enables the runtime computation of k-cores in “live” distributed systems. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world data sets is provided.read more
Citations
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Identifying and ranking influential spreaders in complex networks by neighborhood coreness
Joonhyun Bae,Sangwook Kim +1 more
TL;DR: A novel measure, coreness centrality, is proposed, to estimate the spreading influence of a node in a network using the k-shell indices of its neighbors, which can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.
Journal ArticleDOI
K-core decomposition of large networks on a single PC
TL;DR: A thorough analysis of all algorithms concluding that it is viable to compute k-core decomposition for large networks in a consumer-grade PC and an optimized implementation of an external-memory algorithm by Cheng, Ke, Chu, and Ozsu is presented.
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A survey of community search over big graphs
TL;DR: A comprehensive review of existing community search works can be found in this paper, where the authors analyze and compare the quality of communities under their models, and the performance of different solutions.
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Efficient Core Maintenance in Large Dynamic Graphs
TL;DR: Wang et al. as discussed by the authors proposed a new efficient algorithm to maintain the core number for every node in a dynamic graph, where only certain nodes need to update their core numbers given the graph is changed by inserting/deleting an edge.
Proceedings ArticleDOI
Julienne: A Framework for Parallel Graph Algorithms using Work-efficient Bucketing
TL;DR: The Julienne framework is developed, which extends a recent shared-memory graph processing framework called Ligra with an interface for maintaining a collection of buckets under vertex insertions and bucket deletions, and develops the first work-efficient parallel algorithm for k-core in the literature with nontrivial parallelism.
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