Scalable Computing of Betweenness Centrality based on Graph Reduction with a Case Study on Breast Cancer Analytics

Abstract BackgroundGraph theory has been widely applied to the studies in biomedicine such as structural measures including betweenness centrality. However, if the network size is too large, the result of betweenness centrality would be difficult to obtain in a reasonable amount of time.ResultIn this paper, we describe an approach, 1+ɛ lossy graph reduction algorithm, to computing betweenness centrality on large graphs. The approach is able to guarantee a bounded approximation result. We use GSE48216, a breast cancer cell line co-expression network, to show that our algorithms can achieve a higher reduction rate with a trade-off of some bounded errors in query results. Furthermore, by comparing the betweenness centrality of the original graph and the reduced graph, it can be shown that a higher reduction rate does not sacrifice the accuracy of betweenness centrality when providing faster execution time.ConclusionsOur proposed 1+ɛ lossy graph reduction algorithm is validated by the experiment results which show that the approach achieves a faster execution within a bounded error rate.

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A Semantic Approach to Data Reduction for Weighted Graphs and Complex Queries

International Journal of Semantic Computing ◽

10.1142/s1793351x18500010 ◽

2018 ◽

Vol 12 (02) ◽

pp. 287-312 ◽

Cited By ~ 1

Author(s):

Shaoting Wang ◽

Guigang Zhang ◽

Phillip Sheu ◽

Masahiro Hayakawa ◽

Hiroyuki Shigematsu ◽

...

Keyword(s):

Data Reduction ◽

Synthesis Method ◽

Reduction Rate ◽

Critical Issue ◽

Complex Data ◽

Graph Reduction ◽

Semantic Approach ◽

Original Graph ◽

Graph Data ◽

Graph Problems

Graphs are widely used to store complex data nowadays: social networks, recommendation engines, computer networks and bioinformatics, to name a few. With a rapidly growing amount of data on the Internet in recent years, designing scalable systems to process the huge graph data efficiently has become a critical issue. In order to store and process the graph data efficiently in memory/disk, as well as to save time for transferring the data, graph compression techniques are often used. However, most of the existing graph data compression approaches are syntactic, which means they focus on graph structure and reduce it by serialization or redundancy removal. In this paper we focus on a semantic approach, namely query-based graph data reduction, which reduces a graph by preserving only the information relevant to the queries needed by an application. We study several classical graph problems and their applications, and design a suite of graph reduction algorithms to generate reduced graphs in which an application can still compute the same solutions. In addition, we design a synthesis method that can combine existing graph reduction algorithms to generate a reduced graph for a complex graph problem that includes more than one constraint. We also discuss incremental maintenance in order to update a reduced graph without reprocessing the whole graph again when the original graph is modified. We conduct experiments to compare the reduction rate of our algorithms with different sizes and types of data.

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