Graph sampling by lagged random walk

C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis ◽

10.1109/sc41405.2020.00060 ◽

2020 ◽

Author(s):

Santosh Pandey ◽

Lingda Li ◽

Adolfy Hoisie ◽

Xiaoye S. Li ◽

Hang Liu

Keyword(s):

Random Walk ◽

Graph Sampling

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Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs

10.1109/pact52795.2021.00029 ◽

2021 ◽

Author(s):

Pengyu Wang ◽

Chao Li ◽

Jing Wang ◽

Taolei Wang ◽

Lu Zhang ◽

...

Keyword(s):

Random Walk ◽

Graph Sampling

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On random walk based graph sampling

2015 IEEE 31st International Conference on Data Engineering ◽

10.1109/icde.2015.7113345 ◽

2015 ◽

Cited By ~ 20

Author(s):

Rong-Hua Li ◽

Jeffrey Xu Yu ◽

Lu Qin ◽

Rui Mao ◽

Tan Jin

Keyword(s):

Random Walk ◽

Graph Sampling

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Erratum: On Random Walk Based Weighted Graph Sampling [IEICE Transactions on Information and Systems Vol.E101.D (2018) , No.2 pp.535-538]

IEICE Transactions on Information and Systems ◽

10.1587/transinf.2018ede0006 ◽

2018 ◽

Vol E101.D (7) ◽

pp. 1980_e1-1980_e1

Author(s):

Jiajun ZHOU ◽

Bo LIU ◽

Lu DENG ◽

Yaofeng CHEN ◽

Zhefeng XIAO

Keyword(s):

Random Walk ◽

Weighted Graph ◽

Graph Sampling

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Applying graph sampling methods on student model initialization in intelligent tutoring systems

International Journal of Information and Learning Technology ◽

10.1108/ijilt-03-2016-0011 ◽

2016 ◽

Vol 33 (4) ◽

pp. 202-218 ◽

Cited By ~ 3

Author(s):

Marija Vištica ◽

Ani Grubišic ◽

Branko Žitko

Keyword(s):

Random Walk ◽

Intelligent Tutoring Systems ◽

Domain Knowledge ◽

Sampling Methods ◽

Intelligent Tutoring ◽

Student Model ◽

Content Type ◽

Tutoring Systems ◽

Graph Sampling ◽

Sampling Algorithms

Purpose – In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue. Design/methodology/approach – In order to generate a knowledge sample that represents truly a certain domain knowledge, the authors can use sampling algorithms. In this paper, the authors present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball and Represent algorithm) and investigate which structural properties of the domain knowledge sample are preserved after sampling process is conducted. Findings – The samples that the authors got using these algorithms are compared and the authors have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to find the best one. Originality/value – This approach is original as the authors could not find any similar work that uses graph sampling methods for student modeling.

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