TY - GEN
T1 - Rapid analysis of network connectivity
AU - Freitas, Scott
AU - Tong, Hanghang
AU - Cao, Nan
AU - Xia, Yinglong
N1 - Funding Information:
5 ACKNOWLEDGEMENTS This work is partially supported by the National Science Foundation under Grant No. IIS-1651203, IIS-1715385 and IIS-1743040, by DTRA under the grant number HDTRA1-16-0017, by Army Research Office under the contract number W911NF-16-1-0168, and gifts from Huawei and Baidu.
Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - This research focuses on accelerating the computational time of two base network algorithms (k-simple shortest paths and minimum spanning tree for a subset of nodes)-cornerstones behind a variety of network connectivity mining tasks-with the goal of rapidly finding network pathways and trees using a set of user-specific query nodes. To facilitate this process we utilize: (1) multi-threaded algorithm variations, (2) network re-use for subsequent queries and (3) a novel algorithm, Key Neighboring Vertices (KNV), to reduce the network search space. The proposed KNV algorithm serves a dual purpose: (a) to reduce the computation time for algorithmic analysis and (b) to identify key vertices in the network (context). Empirical results indicate this combination of techniques significantly improves the baseline performance of both algorithms. We have also developed a web platform utilizing the proposed network algorithms to enable researchers and practitioners to both visualize and interact with their datasets (PathFinder: http://www.path-finder.io).
AB - This research focuses on accelerating the computational time of two base network algorithms (k-simple shortest paths and minimum spanning tree for a subset of nodes)-cornerstones behind a variety of network connectivity mining tasks-with the goal of rapidly finding network pathways and trees using a set of user-specific query nodes. To facilitate this process we utilize: (1) multi-threaded algorithm variations, (2) network re-use for subsequent queries and (3) a novel algorithm, Key Neighboring Vertices (KNV), to reduce the network search space. The proposed KNV algorithm serves a dual purpose: (a) to reduce the computation time for algorithmic analysis and (b) to identify key vertices in the network (context). Empirical results indicate this combination of techniques significantly improves the baseline performance of both algorithms. We have also developed a web platform utilizing the proposed network algorithms to enable researchers and practitioners to both visualize and interact with their datasets (PathFinder: http://www.path-finder.io).
KW - K-simple shortest paths
KW - MST
KW - Multi-threading
KW - Network visualization
KW - Parallel processing
KW - Search space reduction
KW - Seed nodes
UR - http://www.scopus.com/inward/record.url?scp=85037350535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037350535&partnerID=8YFLogxK
U2 - 10.1145/3132847.3133170
DO - 10.1145/3132847.3133170
M3 - Conference contribution
AN - SCOPUS:85037350535
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2463
EP - 2466
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -