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Fast crawling methods of exploring content distributed over large graphs


Despite recent effort to estimate topology characteristics of large graphs (e.g., online social networks and peer-to-peer networks), little attention has been given to develop a formal crawling methodology to characterize the vast amount of content distributed over these networks. Due to the large-scale nature of these networks and a limited query rate imposed by network service providers, exhaustively crawling and enumerating content maintained by each vertex is computationally prohibitive. In this paper, we show how one can obtain content properties by crawling only a small fraction of vertices and collecting their content. We first show that when sampling is naively applied, this can produce a huge bias in content statistics (i.e., average number of content replicas). To remove this bias, one may use maximum likelihood estimation to estimate content characteristics. However, our experimental results show that this straightforward method requires to sample most vertices to obtain accurate estimates. To address this challenge, we propose two efficient estimators: special copy estimator (SCE) and weighted copy estimator (WCE) to estimate content characteristics using available information in sampled content. SCE uses the special content copy indicator to compute the estimate, while WCE derives the estimate based on meta-information in sampled vertices. We conduct experiments on a variety of real-word and synthetic datasets, and the results show that WCE and SCE are cost effective and also “asymptotically unbiased”. Our methodology provides a new tool for researchers to efficiently query content distributed in large-scale networks.

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The authors wish to thank the anonymous reviewers for their helpful feedback. This work was supported in part by Army Research Office Contract W911NF-12-1-0385, and ARL under Cooperative Agreement W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied of the ARL, or the U.S. Government. The work was also supported in part by National Natural Science Foundation of China (61603290, 61602371, U1301254), Ministry of Education and China Mobile Research Fund (MCM20160311), China Postdoctoral Science Foundation (2015M582663), Natural Science Basic Research Plan in Zhejiang Province of China (LGG18F020016), Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6034, 2017JM6095), Shenzhen Basic Research Grant (JCYJ20160229195940462).

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Correspondence to Junzhou Zhao.



MLE method

We present the MLE of \({\varvec{\omega }}\) only for graph sampling method UNI, because it is not easy to derive the MLE of \({\varvec{\omega }}\) for other graph sampling methods such as RW, MHRW, and FS. Suppose that the graph size is known (this can be estimated by sampling methods proposed in [22]), \(n < |V|\) vertices are sampled and then each copy of \(\mathbf {c}\) is sampled with the same probability \(p=\frac{n}{|V|}\). For simplicity, we assume that content is distributed over networks uniformly at random. Let M be the maximum number of copies that content has. Denote \(P_{i,j}\) as the probability of sampling i copies for content totally having j copies, where \( 1 \le i \le j \le M\). Let \(q=1-p\), we have \(P_{i,j}=\frac{\left( {\begin{array}{c}j\\ i\end{array}}\right) p^i q^{j-i}}{1 - q^j}\). We compute the MLE of \({\varvec{\omega }}\) from sampled content copies in respect to the following two cases:

Case 1 When the content label under study is the number of copies associated with content. For randomly sampled content, let \(\alpha _i\) (\(1\le i\le M\)) be the probability that it has i copies sampled. Among sampled content, let \(x_i\) be the fraction of content with i copies sampled. We have \(\mathbb {E}(x_i) = \alpha _i\). Thus, \(x_i\) is an unbiased estimate of \(\alpha _i\). Next, we present a method to estimate \({\varvec{\omega }}\) based on the relationship of \(\alpha _i\) and \({\varvec{\omega }}\). The likelihood function of \(\alpha _i\) is

$$\begin{aligned} \alpha _{i}=\sum _{j=i}^{M} \omega _j P_{i,j}. \end{aligned}$$

This is similar to packet sampling-based flow size distribution estimation studied in [12], where each packet is sampled with probability p. Here a flow refers to a group of packets with the same source and destination, and the flow size is the number of packets that it contains. In our context, content corresponds to a flow, and its copies correspond to packets in the flow. Therefore, we can develop a maximum likelihood estimate \(\hat{\omega }_k^{\text {MLE}}\) of \(\omega _k\) (\(1\le k\le M\)) similar to the method proposed in [12].

Case 2 When the content label under study is independent with the number of duplicates and it is available in each content copy, which is not a latent property such as the number of copies content has, we use the following approach to derive the MLE. Define \(\beta _{k,j}\) (\(0\le k\le K\), \(1\le j\le M\)) as the fraction of the number of content with label \(l_k\) and j copies over the number of content with label \(l_k\). For sampled content, let \(\alpha _{k,i}\) (\(1\le i\le M\)) be the probability that its content label is \(l_k\) and has i copies sampled. Then, the likelihood function of \(\alpha _{k,i}\) is

$$\begin{aligned} \alpha _{k,i}=\sum _{j=i}^M \beta _{k,j} P_{i,j}. \end{aligned}$$

\(\alpha _{k,i}\) can be estimated based on sampled content copies. That is, among sampled content, let \(x_{k,i}\) be the fraction of content with label \(l_k\) that has i copies sampled. We have \(\mathbb {E}(x_{k,i}) = \alpha _{k,i}\). Therefore, \(x_{k,i}\) is an unbiased estimate of \(\alpha _{k,i}\). Similar to (5), we then develop a maximum likelihood estimate \(\hat{\beta }_{k,j}\) of \(\beta _{k,j}\), \(1\le j\le M\). Since

$$\begin{aligned} \alpha _k=\omega _k \sum _{i=1}^M \sum _{j=i}^M \beta _{k,j} P_{i,j}, \end{aligned}$$

we have the following estimator of \(\omega _k\)

$$\begin{aligned} \hat{\omega }_k^{\text {MLE}}=\frac{\hat{\alpha }_k}{S^{\text {MLE}}\sum _{i=1}^M \sum _{j=i}^M \hat{\beta }_{k,j} P_{i,j}}, \quad 0\le k\le K, \end{aligned}$$

where \(\hat{\alpha }_k\) is the fraction of sampled content with label \(l_k\), and

$$\begin{aligned} S^{\text {MLE}}=\sum _{k=0}^K \frac{\hat{\alpha }_k}{\sum _{i=1}^M \sum _{j=i}^M \hat{\beta }_{k,j} P_{i,j}}. \end{aligned}$$

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Wang, P., Zhao, J., Lui, J.C.S. et al. Fast crawling methods of exploring content distributed over large graphs. Knowl Inf Syst 59, 67–92 (2019).

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  • Crawling
  • Online social networks
  • Sampling
  • Random walks