KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

Borassi, Michele; Natale, Emanuele

doi:10.1145/3284359

Cited by 47 publications

(74 citation statements)

References 49 publications

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“…The results show that DrBC can effectively induce the partial-order relations of nodes regarding their BC from the embedding space. Our method achieves at least comparable accuracy on both synthetic and real-world networks to state-of-the-art samplingbased baselines, and much better performance than the top-N % dedicated baselines [5] and traditional node embedding models, such as Node2Vec [19]. In terms of running time, our model is far more efficient than sampling-based baselinesïĳŇ node embedding based regressors, and is comparable to the top-N % dedicated baselines.…”

Section: Introductionmentioning

confidence: 81%

Learning to Identify High Betweenness Centrality Nodes from Scratch

Fan

Zeng

Ding

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Betweenness centrality (BC) is one of the most used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction of shortest paths that pass through them. It is key to many valuable applications, including community detection and network dismantling. Computing BC scores on large networks is computationally challenging due to high time complexity. Many approximation algorithms have been proposed to speed up the estimation of BC, which are mainly sampling-based. However, these methods still prone to considerable execution time on large-scale networks, and their results are often exacerbated when small changes happen to the network structures.In this paper, we focus on identifying nodes with high BC in a graph, since many application scenarios are built upon retrieving nodes with top-k BC. Different from previous heuristic methods, we turn this task into a learning problem and design an encoderdecoder based framework to resolve the problem. More specifically, the encoder leverages the network structure to encode each node into an embedding vector, which captures the important structural information of the node. The decoder transforms the embedding vector for each node into a scalar, which captures the relative rank of this node in terms of BC. We use the pairwise ranking loss to train the model to identify the orders of nodes regarding their BC. By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes. Comprehensive experiments on both synthetic and real-world networks demonstrate that, compared to representative baselines, our model drastically speeds up the prediction without noticeable sacrifice in accuracy, and outperforms the state-of-the-art by accuracy on several large real-world networks.

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Section: Introductionmentioning

confidence: 81%

Learning to Identify High Betweenness Centrality Nodes from Scratch

Fan

Zeng

Ding

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

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“…15: for all v ∈ π do 16: x(v) ← x(v) + 1 17: end for 18: τ ← τ + 1 19: end for 20: end while 21: return x/τ Betweenness centrality is estimated as: b = x/τ . 22: end procedure of nodes and edges, several approximation algorithms have been devised [16,[21][22][23][24]. These algorithms trade solution quality for speed and can be much faster.…”

Section: Betweenness Approximation As Concrete Examplementioning

confidence: 99%

“…For illustration purposes, we put ourselves now in the shoes of the authors of the most recent of the cited algorithms, which is called KADABRA [16]: we describe (some of) the necessary steps in the process of writing an algorithm engineering paper on KADABRA 4 -with a focus on the design and evaluation of the experiments.…”

Section: Betweenness Approximation As Concrete Examplementioning

confidence: 99%

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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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“…They used Rademacher average [29] to determine the number of required samples. Finally, Borassi and Natale [6] presented the KADABRA algorithm, which uses balanced bidirectional BFS (bb-BFS) to sample shortest paths. In bb-BFS, a BFS is performed from each of the two endpoints s and t, in such a way that they are likely to explore about the same number of edges.…”

Section: Approximate Algorithmsmentioning

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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KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

Cited by 47 publications

References 49 publications

Learning to Identify High Betweenness Centrality Nodes from Scratch

Learning to Identify High Betweenness Centrality Nodes from Scratch

Guidelines for Experimental Algorithmics: A Case Study in Network Analysis

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

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