TY - GEN
T1 - Sampling multiple nodes in large networks
T2 - 15th ACM International Conference on Web Search and Data Mining, WSDM 2022
AU - Ben-Eliezer, Omri
AU - Eden, Talya
AU - Oren, Joel
AU - Fotakis, Dimitris
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/2/11
Y1 - 2022/2/11
N2 - Sampling random nodes is a fundamental algorithmic primitive in the analysis of massive networks, with many modern graph mining algorithms critically relying on it. We consider the task of generating a large collection of random nodes in the network assuming limited query access (where querying a node reveals its set of neighbors). In current approaches, based on long random walks, the number of queries per sample scales linearly with the mixing time of the network, which can be prohibitive for large real-world networks. We propose a new method for sampling multiple nodes that bypasses the dependence in the mixing time by explicitly searching for less accessible components in the network. We test our approach on a variety of real-world and synthetic networks with up to tens of millions of nodes, demonstrating a query complexity improvement of up to x20 compared to the state of the art.
AB - Sampling random nodes is a fundamental algorithmic primitive in the analysis of massive networks, with many modern graph mining algorithms critically relying on it. We consider the task of generating a large collection of random nodes in the network assuming limited query access (where querying a node reveals its set of neighbors). In current approaches, based on long random walks, the number of queries per sample scales linearly with the mixing time of the network, which can be prohibitive for large real-world networks. We propose a new method for sampling multiple nodes that bypasses the dependence in the mixing time by explicitly searching for less accessible components in the network. We test our approach on a variety of real-world and synthetic networks with up to tens of millions of nodes, demonstrating a query complexity improvement of up to x20 compared to the state of the art.
KW - Graph and network sampling
KW - Node sampling
UR - http://www.scopus.com/inward/record.url?scp=85125763059&partnerID=8YFLogxK
U2 - 10.1145/3488560.3498383
DO - 10.1145/3488560.3498383
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AN - SCOPUS:85125763059
T3 - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
SP - 37
EP - 47
BT - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
Y2 - 21 February 2022 through 25 February 2022
ER -