Sampling social networks using shortest paths

Rezvanian, Alireza; Meybodi, Mohammad Reza

doi:10.1016/j.physa.2015.01.030

Cited by 41 publications

(15 citation statements)

References 60 publications

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“…Another simpler search based sampling technique is the random node-neighbor (RNN) [19] algorithm which randomly selects a set of seed nodes, takes the neighbors in a single hop and induces the edges of the resulting vertex set. Searching for shortest paths (SP) [33] between randomly sampled nodes can be used for selecting sequences of nodes and edges to induce a subsampled graph.…”

Section: Edge Samplingmentioning

confidence: 99%

Little Ball of Fur

Rózemberczki

Kiss²,

Sarkar

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and students in a single streamlined framework. We created this framework with a focus on a coherent application public interface which has a convenient design, generic input data requirements, and reasonable baseline settings of algorithms. Here we overview these design foundations of the framework in detail with illustrative code snippets. We show the practical usability of the library by estimating various global statistics of social networks and web graphs. Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features.

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Section: Edge Samplingmentioning

confidence: 99%

Little Ball of Fur

Rózemberczki

Kiss²,

Sarkar

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…Each edge occurring in the shortest paths between many pairs of selective nodes are ranked based on the number of times they appear in various shortest paths. The subnetwork (sampled network) that includes a specified percentage of the highly ranked edges is then extracted and used for further analysis [35].…”

Section: Role Of Shortest Path Analysis In Complex Networkmentioning

confidence: 99%

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Thilaga

Ramasamy

Nadarajan

et al. 2018

J. Integr. Neurosci.

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Understanding and analyzing the dynamic interactions among millions of spatially distributed and functionally connected regions in the human brain constituting a massively parallel communication system is one of the major challenges in computational neuroscience. Many studies in the recent past have employed graph theory to efficiently model, quantitatively analyze, and understand the brain's electrical activity. Since, the human brain is believed to broadcast information with reduced material and metabolic costs, identifying various brain regions in the shortest pathways of information dissemination becomes essential to understand the intricacies of brain function. This paper proposes a graph theoretic approach using the concept of shortest communication paths between various brain regions (electrode sites) to identify the significant communication pathways of information exchange between various nodes in the functional brain networks constructed from multi-channel electroencephalograph data. A special weighted network called the Shortest Path Network is constructed from a functional brain network where the edge weight is computed as the sum of frequency of occurrence of that edge in all possible shortest paths between every pair of electrodes. The weighted Shortest Path Networks thus constructed portray information on the number of times the edges are used in information propagation during different cognitive states. This network is further analyzed by computing the influential communication paths to characterize the information dissemination among brain regions during different cognitive load conditions. The experimental results presented demonstrate the efficacy of this novel graph theoretic approach in identifying, quantifying, and comparing the significant shortest pathways of information exchange during mild and heavy cognitive load conditions. Analysis also suggests that future research should consider the biological inferences of the ability of the human brain to use reduced material and metabolic cost during the instantaneous transmission of information.

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“…In this experiment, e±ciency of the proposed sampling algorithms (SST and PSST) are compared with some of the existing two-phase sampling algorithms including shortest path sampling (SPS) 31 and DLAS. 30 For this experiment, sampling rate is set to 30%.…”

Section: Experiments IVmentioning

confidence: 99%

Social network sampling using spanning trees

Jalali

Rezvanian

Meybodi

2016

Int. J. Mod. Phys. C

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Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a sub-graph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on well-known real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of the proposed sampling algorithms in terms of Kolmogorov–Smirnov distance (KSD), skew divergence distance (SDD) and normalized distance (ND).

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Sampling social networks using shortest paths

Cited by 41 publications

References 60 publications

Little Ball of Fur

Little Ball of Fur

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Social network sampling using spanning trees

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