Hierarchical Decomposition for Betweenness Centrality Measure of Complex Networks

Li, Yong; Li, Wenguo; Tan, Yi; Liu, Fang; Cao, Yijia; Lee, Kwang Y.

doi:10.1038/srep46491

Cited by 38 publications

(41 citation statements)

References 35 publications

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“…In particular, the higher the number of clusters, the closer to 1 the AS H/B p=1 . We can thus conclude that our solution always outperforms the one in [13] with synthetic graphs. p=1 is computed again using Eq.…”

Section: Synthetic Graphs Analysismentioning

confidence: 53%

“…Brandes' algorithm, labelled as B and with the solution proposed in [13], labelled with H. We chose this algorithm for comparison because it belongs to the same category as ours (cluster-based computation). However, due to the unavailability of source/executable code for H, we only consider the AS metric in sequential mode, by relying on the indications provided by the authors in the paper for its computation (see Eq.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Fast Cluster-based Computation of Exact Betweenness Centrality in Large Graphs

Daniel

Furno

Goglia

et al. 2021

Preprint

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Nowadays a large amount of data is originated by complex systems, such as social networks, transportation systems, computer and service networks. These systems can be modeled by using graphs and studied by exploiting graph metrics, such as Betweenness Centrality (BC), a popular metric to analyze node centrality of graphs. In spite of its great potential, this metric requires long computation time, especially for large graphs. In this paper, we present a very fast algorithm to compute BC of undirected graphs by exploiting clustering. The algorithm leverages structural properties of graphs to find classes of equivalent nodes: by selecting one representative node for each class, we are able to compute BC by significantly reducing the number of single-source shortest path explorations adopted by Brandes' algorithm. We formally prove the graph properties that we exploit to define the algorithm and present an implementation based on Scala for both sequential and parallel map-reduce executions. The experimental evaluation of both versions, conducted with synthetic and real graphs, reveals that our solution largely outperforms Brandes' algorithm and significantly improves known heuristics.

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Section: Synthetic Graphs Analysismentioning

confidence: 53%

Section: Experimental Evaluationmentioning

confidence: 99%

Fast Cluster-based Computation of Exact Betweenness Centrality in Large Graphs

Daniel

Furno

Goglia

et al. 2021

Preprint

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show abstract

“…Degree centrality is a measure of the importance of a single node, and it describes the number of edges connecting nodes [ 27 ]. Betweenness centrality is the shortest path through which a particular node is analyzed [ 28 ]. Eigenvector centrality takes into account the degree of itself and the degree of its neighbors [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

Chong

Peng

Sun

et al. 2020

BioMed Research International

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Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

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“…This algorithm also relies on partitioning, causing all computations to be run "over graphs that are significantly smaller than the original graph" [34]. Baglioni et al rely on the sparseness of social networks to reduce the size of graphs [35], and work by Li et al [36] relies on the inherent community structure of real-world networks to do "hierarchical decomposition" of a graph. These strategies speed computation by shrinking the size of the graph that must be computed.…”

Section: Introductionmentioning

confidence: 99%