On Sampling Nodes in a Network

Chiericetti, Flavio; Dasgupta, Anirban; Kumar, Ravi; Lattanzi, Silvio; Sarlós, Tamás

doi:10.1145/2872427.2883045

Cited by 54 publications

(45 citation statements)

References 28 publications

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“…We consider 13 real-world undirected network datasets from SNAP 4 and Network Repository 5 , which are listed in an ascending order of graph size in Table 1. 6 We classify four graphs whose sizes are smaller than 50K as small graphs and the rest of them as large graphs. In this experimental evaluation, we focus on computing the Kemeny's constant K of a simple random walk on the largest strongly connected components (LSCC) of a graph, as used in [28].…”

Section: Experiments Resultsmentioning

confidence: 99%

An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs

Xin

Lee

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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The mean hitting time of a Markov chain on a graph from an arbitrary node to a target node randomly chosen according to its stationary distribution is called Kemeny's constant, which is an important metric for network analysis and has a wide range of applications. It is, however, still computationally expensive to evaluate the Kemeny's constant, especially when it comes to a large graph, since it requires the computation of the spectrum of the corresponding transition matrix or its normalized Laplacian matrix. In this paper, we propose a simple yet computationally efficient Monte Carlo algorithm to approximate the Kemeny's constant, which is equipped with an ( , )-approximation estimator. Thanks to its inherent algorithmic parallelism, we are able to develop its parallel implementation on a GPU to speed up the computation. We provide extensive experiment results on 13 real-world graphs to demonstrate the computational efficiency and scalability of our algorithm, which achieves up to 500× speed-up over the state-ofthe-art algorithm. We further present its practical enhancements to make our algorithm ready for practical use in real-world settings.

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Section: Experiments Resultsmentioning

confidence: 99%

An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs

Xin

Lee

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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“…Different from the bootstrap sampling methods, we focus on sampling subgraphs from large networks. To our knowledge, although a variety of graph sampling techniques have been introduced in [4], [11], our approach is the first work that combines link prediction characteristics [22] with graph sampling methods to achieve the high link prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Approach to Link Prediction

Liang

Aggarwal

et al. 2017

IEEE Trans. Knowl. Data Eng.

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A network with n nodes contains O(n 2) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity on large networks. Hence, most known link prediction methods are designed for evaluating the link propensity on a specified subset of links, rather than on the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this article, we propose an ensemble enabled approach to scaling up link prediction, by decomposing traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with latent factor models, which can be effectively implemented on networks of modest size. By incorporating with the characteristics of link prediction, the ensemble approach further reduces the sizes of subproblems without sacrificing its prediction accuracy. The ensemble enabled approach has several advantages in terms of performance, and our experimental results demonstrate the effectiveness and scalability of our approach.

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“…When the complete structure of a network is not available, a network sampling is often used to obtain a partial structure of the network [22], [23], [26]- [30], [33]. If arbitrary access to any nodes is allowed, random sampling seems to be a natural choice for simplicity because of its simplicity and neutrality.…”

Section: Introductionmentioning

confidence: 99%

“…If arbitrary access to any nodes is allowed, random sampling seems to be a natural choice for simplicity because of its simplicity and neutrality. However, in many real applications, random access to nodes is not allowed [30], [33], so crawl-based sampling techniques have been widely used for analyzing the structure of several types of large-scale networks, such as online social networks [23], the world wide web [34], and peer-topeer (P2P) networks [35]. When using crawl-based network sampling, it is assumed that only one node can be visited initially, but the neighbors of already visited nodes can be visited at each step.…”

Section: Introductionmentioning

confidence: 99%

Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set

Tsugawa

Ohsaki

2020

IEEE Access

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We study the problem of identifying a set of key nodes from a network when limited knowledge about its structure is available. Most studies assume complete knowledge of the given network when identifying a set of key nodes, but in current practice, networks of interest are often too huge to obtain their entire topological structures. When the complete structure of a network is not available, network sampling strategies are often used to obtain a partial structure of the network. We investigate how network sampling strategies affect the problem of identifying a key node set. Specifically, we investigate the effect of conventional network sampling strategies on the solutions found for two types of key node set identification problems: the minimum p-median problem and the influence maximization problem. Our results show that when the network is obtained using crawl-based network sampling strategies, both the minimum pmedian and the influence maximization problems are effectively solved by simple heuristic algorithms with sampling ratios in the 10-20% range. We also find that among three conventional sampling strategies (random sampling, random walk sampling, and sample edge counts) checked in this paper, random walk sampling is the most robust strategy in terms of effectively identifying the key node sets of diverse types of networks.INDEX TERMS Influence maximization problem, key node set identification, minimum p-median problem, network sampling, social networks.

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On Sampling Nodes in a Network

Cited by 54 publications

References 28 publications

An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs

An Efficient and Scalable Algorithm for Estimating Kemeny's Constant of a Markov Chain on Large Graphs

An Ensemble Approach to Link Prediction

Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set

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