Sampling community structure

Maiya, Arun S.; Berger-Wolf, Tanya Y.

doi:10.1145/1772690.1772762

Cited by 144 publications

(98 citation statements)

References 23 publications

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“…Related to our work here are the works that study the effects of missing data on measured properties of social networks [12] and the study of biases when obtaining a graph of the Internet based on measurements [14,1]. Another related line of work is on sampling in large networks [16,20,25], where given a large network we would like to find some procedure to sample a small set of nodes such that important structural properties of the network are preserved.…”

Section: Introductionmentioning

confidence: 99%

Correcting for missing data in information cascades

Sadikov

Medina

Leskovec

et al. 2011

Proceedings of the Fourth ACM International Conference on Web Search and Data Mining

101

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Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusion. In such cases we say that a contagion spreads through the network, a process that can be modeled by a cascade graph. Studying cascades and network diffusion is challenging due to missing data. Even a single missing observation in a sequence of propagation events can significantly alter our inferences about the diffusion process.We address the problem of missing data in information cascades. Specifically, given only a fraction C ′ of the complete cascade C, our goal is to estimate the properties of the complete cascade C, such as its size or depth. To estimate the properties of C, we first formulate ktree model of cascades and analytically study its properties in the face of missing data. We then propose a numerical method that given a cascade model and observed cascade C ′ can estimate properties of the complete cascade C. We evaluate our methodology using information propagation cascades in the Twitter network (70 million nodes and 2 billion edges), as well as information cascades arising in the blogosphere. Our experiments show that the k-tree model is an effective tool to study the effects of missing data in cascades. Most importantly, we show that our method (and the k-tree model) can accurately estimate properties of the complete cascade C even when 90% of the data is missing.

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Section: Introductionmentioning

confidence: 99%

Correcting for missing data in information cascades

Sadikov

Medina

Leskovec

et al. 2011

Proceedings of the Fourth ACM International Conference on Web Search and Data Mining

101

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“…Maiya et al [8] proposed two sampling algorithm based on the notion that samples with good expansion properties tend to be more representative of the clustering structure. Newly sampled nodes are chosen either deterministically or probabilistically and the process is continued until we reach the desired subgraph size.…”

Section: Clustering Structure In Graphsmentioning

confidence: 99%

Clustering-Structure Representative Sampling from Graph Streams

Zhang

Zhu

Pei

et al. 2017

Complex Networks &Amp; Their Applications VI

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Most existing sampling algorithms on graphs (i.e., network-structured data) focus on sampling from memory-resident static graphs and assume the entire graphs are always available. However, the graphs encountered in modern applications are often too large and/or too dynamic to be processed with limited memory. Furthermore, existing sampling techniques are inadequate for preserving the inherent clustering structure, which is an essential property of complex networks.To tackle these problems, we propose a new sampling algorithm that dynamically maintains a representative sample and is capable of retaining clustering structure in graph streams at any time. Performance of the proposed algorithm is evaluated through empirical experiments using real-world networks. The experimental results have shown that our proposed CPIES algorithm can produce clustering-structure representative samples and outperforms current online sampling algorithms.

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“…For example, sampling has been used for finding interesting subgraphs [1], communities [11], and graphlet frequency distribution [15].…”

Section: Related Workmentioning

confidence: 99%

“…Bhuiyan et al [15] use it for sampling graphlets, Maiya et al [11] use it for sampling community structure, and Gjoka et al [8] use it for finding an approximate degree distribution of Facebook network. However, each of these works have a different objective and they sample from different population.…”

Section: Related Workmentioning

confidence: 99%

Sampling Triples from Restricted Networks using MCMC Strategy

Rahman

Hasan

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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In large networks, the connected triples are useful for solving various tasks including link prediction, community detection, and spam filtering. Existing works in this direction concern mostly with the exact or approximate counting of connected triples that are closed (aka, triangles). Evidently, the task of triple sampling has not been explored in depth, although sampling is a more fundamental task than counting, and the former is useful for solving various other tasks, including counting. In recent years, some works on triple sampling have been proposed that are based on direct sampling, solely for the purpose of triangle count approximation. They sample only from a uniform distribution, and are not effective for sampling triples from an arbitrary user-defined distribution. In this work we present two indirect triple sampling methods that are based on Markov Chain Monte Carlo (MCMC) sampling strategy. Both of the above methods are highly efficient (several magnitudes faster on large datasets) compared to a direct sampling-based method, specifically for the task of sampling from a non-uniform probability distribution. Another significant advantage of the proposed methods is that they can sample triples from networks that have restricted access, on which a direct sampling based method is simply not applicable.

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Sampling community structure

Cited by 144 publications

References 23 publications

Correcting for missing data in information cascades

Correcting for missing data in information cascades

Clustering-Structure Representative Sampling from Graph Streams

Sampling Triples from Restricted Networks using MCMC Strategy

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