Approximating Betweenness Centrality in Large Evolving Networks

Bergamini, Elisabetta; Meyerhenke, Henning; Staudt, Christian

doi:10.1137/1.9781611973754.12

Cited by 49 publications

(96 citation statements)

References 32 publications

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“…For example, this can be the speedup of a newly proposed algorithm against some base algorithm that can be tested on all systems. As an example, for betweenness centrality, some papers re-implement the Brandes [20] algorithm and compare their performance against it [24,45,53]. As this metric is independent of the hardware, it can even be used to compare implementations on different systems, e. g., CPU versus GPU implementations.…”

Section: System-independent Metricsmentioning

confidence: 99%

Guidelines for Experimental Algorithmics: A Case Study in Network Analysis

et al. 2019

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The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis -an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines -including statistical analyses -for the systematic evaluation of network analysis algorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide SimexPal, a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of SimexPal and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In summary, both our guidelines and SimexPal shall modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts.

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Section: System-independent Metricsmentioning

confidence: 99%

Guidelines for Experimental Algorithmics: A Case Study in Network Analysis

et al. 2019

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“…In another line of research, many research efforts consider approximating betweenness centrality [30], [31], and some more recent efforts offer approximation methods to compute the changes of betweenness centrality values on graph updates [32], [33]. In contrast, our work deals with the exact calculation of betweenness centrality.…”

Section: Related Workmentioning

confidence: 99%

Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Jamour

Skiadopoulos

Kalnis

2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose iCENTRAL; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. iCENTRAL is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, iCENTRAL scales to large graphs. We demonstrate with real datasets that the serial implementation of iCENTRAL is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.

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“…Proposition 1 suggests that for computing betweenness score of r, we first check whether out degree of r is greater than 0 and if so, we compute RV (r). Betweenness score of r is exactly computed using Equation 5.…”

Section: Reachable Verticesmentioning

confidence: 99%

Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

Chehreghani

Bifet

Abdessalem

2018

Advances in Knowledge Discovery and Data Mining

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Graphs (networks) are an important tool to model data in different domains, including social networks, bioinformatics and the world wide web. Most of the networks formed in these domains are directed graphs, where all the edges have a direction and they are not symmetric. Betweenness centrality is an important index widely used to analyze networks. In this paper, first given a directed network G and a vertex r ∈ V (G), we propose a new exact algorithm to compute betweenness score of r. Our algorithm pre-computes a set RV(r), which is used to prune a huge amount of computations that do not contribute in the betweenness score of r. Time complexity of our exact algorithm depends on |RV(r)| and it is respectively Θ(|RV(r)| · |E(G)|) and Θ(|RV(r)| · |E(G)| + |RV(r)| · |V (G)| log |V (G)|) for unweighted graphs and weighted graphs with positive weights. |RV(r)| is bounded from above by |V (G)| − 1 and in most cases, it is a small constant. Then, for the cases where RV(r) is large, we present a simple randomized algorithm that samples from RV(r) and performs computations for only the sampled elements. We show that this algorithm provides an (ǫ, δ)-approximation of the betweenness score of r. Finally, we perform extensive experiments over several real-world datasets from different domains for several randomly chosen vertices as well as for the vertices with the highest betweenness scores. Our experiments reveal that in most cases, our algorithm significantly outperforms the most efficient existing randomized algorithms, in terms of both running time and accuracy. Our experiments also show that our pro-2 Mostafa Haghir Chehreghani et al. posed algorithm computes betweenness scores of all vertices in the sets of sizes 5, 10 and 15, much faster and more accurate than the most efficient existing algorithms.Keywords Networks · directed graphs · betweenness centrality · exact algorithm · approximate algorithm 1 Introduction Graphs (networks) provide an important tool to model data in different domains, including social networks, bioinformatics, road networks, the world wide web and communication systems. A property seen in most of these real-world networks is that the ties between vertices do not always represent reciprocal relations [24]. As a result, the networks formed in these domains are directed graphs where any edge has a direction and the edges are not always symmetric.Centrality is a structural property of vertices (or edges) in the network that quantifies their relative importance. For example, it determines the importance of a person within a social network, or a road within a road network. Freeman [13] introduced and defined betweenness centrality of a vertex as the number of shortest paths from all (source) vertices to all others that pass through that vertex. He used it for measuring the control of a human over the communications among others in a social network [13]. Betweenness centrality is also used in some well-known algorithms for clustering and community detection in social and information networks [15]....

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Approximating Betweenness Centrality in Large Evolving Networks

Cited by 49 publications

References 32 publications

Guidelines for Experimental Algorithmics: A Case Study in Network Analysis

Guidelines for Experimental Algorithmics: A Case Study in Network Analysis

Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

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