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Pool Compression for Undirected Graphs

delete2022-01-01
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OA
AI
M
Muhammad Irfan Yousuf
S
Suhyun Kim *
DOI:10.1109/ACCESS.2022.3179505delete
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摘要

摘要

En 中文
We present a new graph compression scheme that intrinsically exploits the similarity and locality of references in a graph by first ordering the nodes and then merging the contiguous adjacency lists of the graph into blocks to create a pool of nodes. The nodes in the adjacency lists of the graph are encoded by their position in the pool. This simple yet powerful scheme achieves compression ratios better than the previous methods for many datasets tested in this paper and, on average, surpasses all the previous methods. The scheme also provides an easy and efficient access to neighbor queries, e.g., finding the neighbors of a node, and reachability queries, e.g., finding if node u is reachable from node v. We test our scheme on publicly available graphs of different sizes and show a significant improvement in the compression ratio and query access time compared to the previous approaches.
Keyword:
Graph compression
merging adjacency lists
node ordering
Elias-Gamma encoding

期刊

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

K
korea institute of science & technology (kist)
学者数:
1.2W
论文数: 1.3W
被引数: 23
U
university of engineering & technology lahore
学者数:
2.7K
论文数: 2.2K
被引数: 0
引用论文

引用论文

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