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Effective and efficient aggregation on uncertain graphs
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AI
DOI：10.1016/j.fss.2021.07.017.png)
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Large-scale graphs are widely used to model the entities and their complex relations. Uncertain graphs are adopted when the relations between entities contain some uncertainty. However, the inherent uncertainties, which are embedded underlying the data and structure of the graphs derived from various sources introduce difficulties on data analysis. To understand the underlying characteristics of large graphs, graph aggregation techniques are critical. However, the existing graph aggregation techniques are designed for deterministic graphs therefore are not applicable on uncertain graphs. In this paper, we provide the first attempt on addressing the aggregation problem on uncertain graphs. To deal with the computation complexity of the aggregation problem, we propose a heuristic-based aggregation algorithm for uncertain graphs and some optimization methods to improve its efficiency in real world implementation. Besides the optimization, to further speed up the process, we design a parallel aggregation implemen-tation approach. The intensive evaluations on the two datasets, DBLP and Flickr, demonstrate that our proposed algorithms are able to produce high quality aggregation results within reasonable operation time and the parallel implementation accelerates the aggregation by up to 82 times compared with the baseline algorithm.(c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Uncertain graphs
Aggregation
Similarity
Optimization
Parallel
期刊
IF:
2.7
论文数:
7.6K
被引数:
1.5W
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