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Graph sampling approach for reducing computational complexity of large-scale social network

Andry Alamsyah, Yahya Peranginangin, Intan Muchtadi-Alamsyah, Budi Rahardjo, Kuspriyanto
2016 Journal of Innovative Technology and Education  
The performance comparison between natural graph sampling strategies using edge random sampling, node random sampling, and random walks are presented on each selected graph property.  ...  In this paper, we propose a graph sampling approach to reduce social network size, thus reducing computation operations.  ...  Average Path Length calculated by finding the shortest path between all pairs of nodes, adding them up and then dividing by the total number of pairs. 7.  ... 
doi:10.12988/jite.2016.6828 fatcat:rs6jfnxxgrac5e7qwfdwix5fmy

Estimating Shortest Path Length Distributions via Random Walk Sampling [article]

Minhui Zheng, Bruce D. Spencer
2018 arXiv   pre-print
In this paper we propose a method to estimate the shortest path length (SPL) distribution of a network by random walk sampling.  ...  To deal with the unequal inclusion probabilities of dyads (pairs of nodes) in the sample, we generalize the usage of Hansen-Hurwitz estimator and Horvitz-Thompson estimator (and their ratio forms) and  ...  In a primitive graph, a path of length k exits between every pair of nodes for some positive integer k.  ... 
arXiv:1806.01757v2 fatcat:kin74qt3xzgvlmt62w6xjuc3cy

Estimation of maximum achievable end-to-end throughput in IEEE 802.11 based wireless mesh networks

Gayatri Venkatesh, Kuang-Ching Wang
2009 2009 IEEE 34th Conference on Local Computer Networks  
The contention among wireless nodes arising due to the IEEE 802.11 medium access control protocol's channel access mechanism renders the estimation of such network attributes challenging in multi-hop networks  ...  This thesis evaluates Adhoc Probe, one state-of-the-art capacity estimation approach for ad hoc wireless networks and shows that it in fact measures achievable throughput instead of capacity and its estimated  ...  The number of probe samples K and the probe train length N must be chosen based on the network topology and the number of hops in the estimation path.  ... 
doi:10.1109/lcn.2009.5355214 fatcat:dvnutuy46ffypobx6koazvwarm

Sampling social networks using shortest paths

Alireza Rezvanian, Mohammad Reza Meybodi
2015 Physica A: Statistical Mechanics and its Applications  
The proposed sampling method first finds the shortest paths between several pairs of nodes selected according to some criteria.  ...  In this paper, we propose to use the concept of shortest path for sampling social networks.  ...  In the proposed sampling method, we first find the shortest paths between several pairs of nodes which are selected according to some criteria.  ... 
doi:10.1016/j.physa.2015.01.030 fatcat:myd2wjt7vnedzk2piicmathrzy

Using structure indices for efficient approximation of network properties

Matthew J. Rattigan, Marc Maier, David Jensen
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
between pairs of nodes.  ...  We introduce the concept of a network structure index (NSI), a composition of (1) a set of annotations on every node in the network and (2) a function that uses the annotations to estimate graph distance  ...  The authors wish to thank Andrew McCallum, and the students and staff of the Information Extraction and Synthesis Lab at the University of Massachusetts for use of the Rexa data, as well as Cindy Loiselle  ... 
doi:10.1145/1150402.1150443 dblp:conf/kdd/RattiganMJ06 fatcat:bhesw6zgkvdctag5qmjuyoc3ki

Time-based sampling of social network activity graphs

Nesreen K. Ahmed, Fredrick Berchmans, Jennifer Neville, Ramana Kompella
2010 Proceedings of the Eighth Workshop on Mining and Learning with Graphs - MLG '10  
In this paper, we propose a novel sampling algorithm called Streaming Time Node Sampling (STNS) that exploits temporal clustering often found in real social networks.  ...  both averages and distributions of several graph properties.  ...  Acknowledgements We thank Kyle Bowen for providing us with the Twitter data and Indika Kahanda for his help in Facebook data analysis.  ... 
doi:10.1145/1830252.1830253 dblp:conf/mlg/AhmedBNK10 fatcat:ns2wpvpxu5eqtb4kugcx54y5xa

A two-hop neighbor preference-based random network graph model with high clustering coefficient for modeling real-world complex networks

Natarajan Meghanathan, Aniekan Essien, Raven Lawrence
2016 Egyptian Informatics Journal  
The proposed Two-Hop Neighbor Preference (THNP)-based model generates random graphs whose clustering coefficient decreases with increase in node degree: matching closely to several real-world network graphs  ...  In this paper, we propose a random network graph model that gives preference to closing the triangle involving three nodes u, w and v with existing links u-w and w-v (i.e., node v is strictly a twohop  ...  The average path length (used in formula (1)) is the average of the path length across all node pairs in the network.  ... 
doi:10.1016/j.eij.2016.06.008 fatcat:bl4bxquokncihpmivpa3bl3gbu

Towards Scalable Network Delay Minimization [article]

Sourav Medya, Petko Bogdanov, Ambuj Singh
2016 arXiv   pre-print
In this paper, we consider the problem of network delay minimization via node upgrades.  ...  We design scalable and high-quality techniques for the general setting based on sampling and targeted to different models of delay distribution.  ...  Given a sample of node pairs P, |P | = p, the expected average distance among the sampled pairs is an unbiased estimate of the average of all-pair distances (µ): E[ 1 p p i=1 X i ] = µ where X i represents  ... 
arXiv:1609.08228v1 fatcat:ktgptvqqfbby5fpbm5yxzbnpcu

Graph Theory in Brain Networks [article]

Moo K. Chung
2021 arXiv   pre-print
Recent developments in graph theoretic analysis of complex networks have led to deeper understanding of brain networks.  ...  Many complex networks show similar macroscopic behaviors despite differences in the microscopic details.  ...  Acknowelgement This study was in part supported by NIH grants R01 EB022856 and R01 EB028753 and NSF grant MDS-2010778.  ... 
arXiv:2103.05781v1 fatcat:fhxu56pwsfgq5gefcojhuthe5u

Growth and evolution of category fluency network graphs

Rajeet Shrestha, Barclay Shaw, Wojbor Woyczynski, Peter J. Thomas, Thomas Fritsch, Alan J. Lerner
2015 Journal of Systems and Integrative Neuroscience  
Growth was also modeled using an extended cognitive network model. Random subsamples of people or of node pairs were used to model network growth and study preferential attachment.  ...  Network graph analysis is a technique used to analyze relationships between nodes and edges and calculate metrics such as path lengths between nodes and clustering coefficients.  ...  The authors have no financial conflict of interest in the preparation, reporting or conclusions drawn from this project.  ... 
doi:10.15761/jsin.1000103 fatcat:okeu6nyy3vcu7p3w4hotwyaaye

Susceptible-infected-spreading-based network embedding in static and temporal networks

Xiu-Xiu Zhan, Ziyu Li, Naoki Masuda, Petter Holme, Huijuan Wang
2020 EPJ Data Science  
Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths.  ...  Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms.  ...  The source code will be available from the first author based on reasonable request. per unit time step β ∈ {0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} in the SI-spreading-based embedding  ... 
doi:10.1140/epjds/s13688-020-00248-5 fatcat:y764mmp3n5a6pmirgtjawbnbly

Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions [article]

S. Bialonski
2012 arXiv   pre-print
In this thesis we study whether and how interaction networks are influenced by the analysis methodology, i.e. by the way how empirical data is acquired (the spatial and temporal sampling of the dynamics  ...  This challenge has stimulated the development of sophisticated data analysis approaches adopting concepts from network theory: systems are considered to be composed of subsystems (nodes) which interact  ...  Thank you for believing in me, for your encouragement, and for your love.  ... 
arXiv:1208.0800v1 fatcat:ap35lrbkbnbvnb4ohrnuvlbfp4

Applications of graph theory to landscape genetics

Colin J. Garroway, Jeff Bowman, Denis Carr, Paul J. Wilson
2008 Evolutionary Applications  
Network structure was characterized by a higher level of clustering than expected by chance, a short mean path length connecting all pairs of nodes, and a resiliency to the loss of highly connected nodes  ...  Two measures of node centrality were negatively related to both the proportion of immigrants in a node and node snow depth.  ...  Tully, OMNR Districts, and participating trappers for fisher tissue samples. We also are grateful to J.A.G. Jaeger, H.G. Broders, and the Geomatics and Landscape Ecology  ... 
doi:10.1111/j.1752-4571.2008.00047.x pmid:25567802 pmcid:PMC3352384 fatcat:p4zdwoobhfb3zgaje4emb22hq4

A Model for Generating Random Networks with Clustering Coefficient Corresponding to Real-World Network Graphs

Natarajan Meghanathan
2016 International Journal of Control and Automation  
We observe the THNP-model to generate random network graphs wherein the clustering coefficient of a node decreases with increase in node degree (resembling closely to several of the realworld network graphs  ...  ), and the graphs still exhibit a Poisson-style distribution for the node degree and path length.  ...  Acknowledgements This paper is a revised and expanded version of a paper entitled " A Random Network Model with High Clustering Coefficient and Variation in Node Degree," presented at the 4th International  ... 
doi:10.14257/ijca.2016.9.1.15 fatcat:enlw3dthfrghfoaii7pjicwb7a

Shortest Path Distance Approximation Using Deep Learning Techniques

Fatemeh Salehi Rizi, Joerg Schloetterer, Michael Granitzer
2018 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)  
Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications.  ...  In this paper, we utilize vector embeddings learnt by deep learning techniques to approximate the shortest paths distances in large graphs.  ...  In BlogCatalog the average shortest path distance is 2.72 and the distribution of path longer than 5 is prohibitively low. We therefore limit the path length to 5.  ... 
doi:10.1109/asonam.2018.8508763 dblp:conf/asunam/RiziSG18 fatcat:7oxiop5lgrbu7oytwouraxdksy
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