TY - GEN

T1 - NimbleCore

T2 - 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

AU - Govindan, Priya

AU - Soundarajan, Sucheta

AU - Eliassi-Rad, Tina

AU - Faloutsos, Christos

N1 - Funding Information:
This work was supported by NSF under Grant No. CNS-1314603, CNS-1314632, IIS-1408924, and by DTRA HDTRA1-10-1-0120
Publisher Copyright:
© 2016 IEEE.

PY - 2016/11/21

Y1 - 2016/11/21

N2 - We address the problem of estimating core numbers of nodes by reading edges of a large graph stored in external memory. The core number of a node is the highest k-core in which the node participates. Core numbers are useful in many graph mining tasks, especially ones that involve finding communities of nodes, influential spreaders and dense subgraphs. Large graphs often do not fit on the memory of a single machine. Existing external memory solutions do not give bounds on the required space. In practice, existing solutions also do not scale with the size of the graph. We propose NimbleCore, an iterative external-memory algorithm, which estimates core numbers of nodes using O(n log dmax) space, where n is the number of nodes and dmax is the maximum node-degree in the graph. We also show that NimbleCore requires O(n) space for graphs with power-law degree distributions. Experiments on forty-eight large graphs from various domains demonstrate that NimbleCore gives space savings up to 60X, while accurately estimating core numbers with average relative error less than 2.3%.

AB - We address the problem of estimating core numbers of nodes by reading edges of a large graph stored in external memory. The core number of a node is the highest k-core in which the node participates. Core numbers are useful in many graph mining tasks, especially ones that involve finding communities of nodes, influential spreaders and dense subgraphs. Large graphs often do not fit on the memory of a single machine. Existing external memory solutions do not give bounds on the required space. In practice, existing solutions also do not scale with the size of the graph. We propose NimbleCore, an iterative external-memory algorithm, which estimates core numbers of nodes using O(n log dmax) space, where n is the number of nodes and dmax is the maximum node-degree in the graph. We also show that NimbleCore requires O(n) space for graphs with power-law degree distributions. Experiments on forty-eight large graphs from various domains demonstrate that NimbleCore gives space savings up to 60X, while accurately estimating core numbers with average relative error less than 2.3%.

UR - http://www.scopus.com/inward/record.url?scp=85006741676&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006741676&partnerID=8YFLogxK

U2 - 10.1109/ASONAM.2016.7752237

DO - 10.1109/ASONAM.2016.7752237

M3 - Conference contribution

AN - SCOPUS:85006741676

T3 - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

SP - 207

EP - 214

BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

A2 - Kumar, Ravi

A2 - Caverlee, James

A2 - Tong, Hanghang

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 August 2016 through 21 August 2016

ER -