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This paper addresses the challenge of determining community scale to which network nodes belong and introduces an innovative hypothesis testing approach. It begins with a network sampling method that generates sequences of node dependency values, revealing community structure and scale in a waveform-like manner. Subsequently, the study employs wavelet analysis, a signal processing technique, to extract local signal periodicity information from these sequences. This information is then used to develop a test for assessing node membership in specific community scales. The proposed method is applied to both simulated and real-world social network data, with results from the simulated data demonstrating its effectiveness in evaluating node membership in particular community scales. [ABSTRACT FROM AUTHOR]

Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%–30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks. [ABSTRACT FROM AUTHOR]

In the past few years, the storage and the analysis of large-scale and fast evolving networks presents a great challenge. Therefore, a number of different techniques have been proposed for sampling large networks. Studies on network sampling primarily analyze the changes of network properties under the sampling. In general, network exploration techniques approximate the original networks more accurate than random node and link selection. Yet, link selection with additional subgraph induction step outperforms most other techniques. In this paper, we apply subgraph induction also to random walk and forest-fire sampling and evaluate the effects of subgraph induction on the sampling accuracy. We analyze different real-world networks and the changes of their properties introduced by sampling. The results reveal that the techniques with subgraph induction improve the performance of techniques without induction and create denser sample networks with larger average degree. Furthermore, the accuracy of sampling decrease consistently across various sampling techniques, when the sampled networks are smaller. Based on the results of the comparison, we introduce the scheme for selecting the most appropriate technique for network sampling. Overall, the breadth-first exploration sampling proves as the best performing technique. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data. [ABSTRACT FROM AUTHOR]

Any network studied in the literature is inevitably just a sampled representative of its real-world analogue. Additionally, network sampling is lately often applied to large networks to allow for their faster and more efficient analysis. Nevertheless, the changes in network structure introduced by sampling are still far from understood. In this paper, we study the presence of characteristic groups of nodes in sampled social and information networks. We consider different network sampling techniques including random node and link selection, network exploration and expansion. We first observe that the structure of social networks reveals densely linked groups like communities, while the structure of information networks is better described by modules of structurally equivalent nodes. However, despite these notable differences, the structure of sampled networks exhibits stronger characterization by community-like groups than the original networks, irrespective of their type and consistently across various sampling techniques. Hence, rich community structure commonly observed in social and information networks is to some extent merely an artifact of sampling. [ABSTRACT FROM AUTHOR]

In recent years, online social networks (OSN) have emerged as a platform of sharing variety of information about people, and their interests, activities, events and news from real worlds. Due to the large scale and access limitations (e.g., privacy policies) of online social network services such as Facebook and Twitter , it is difficult to access the whole public network in a limited amount of time. For this reason researchers try to study and characterize OSN by taking appropriate and reliable samples from the network. In this paper, we propose to use the concept of shortest path for sampling social networks. The proposed sampling method first finds the shortest paths between several pairs of nodes selected according to some criteria. Then the edges in these shortest paths are ranked according to the number of times that each edge has appeared in the set of found shortest paths. The sampled network is then computed as a subgraph of the social network which contains a percentage of highly ranked edges. In order to investigate the performance of the proposed sampling method, we provide a number of experiments on synthetic and real networks. Experimental results show that the proposed sampling method outperforms the existing method such as random edge sampling, random node sampling, random walk sampling and Metropolis–Hastings random walk sampling in terms of relative error (RE), normalized root mean square error (NMSE), and Kolmogorov–Smirnov (KS) test. [ABSTRACT FROM AUTHOR]

Abstract: A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others. [Copyright &y& Elsevier]