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Faster random walks by rewiring online social networks onthefly
 In ICDE
, 2013
"... Abstract — Many online social networks feature restrictive web interfaces which only allow the query of a user’s local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chai ..."
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Abstract — Many online social networks feature restrictive web interfaces which only allow the query of a user’s local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chain Monte Carlo methods such as random walks to sample the social network and support analytics based on the samples. The problem with such an approach, however, is the large amount of queries often required (i.e., a long “mixing time”) for a random walk to reach a desired (stationary) sampling distribution. In this paper, we consider a novel problem of enabling a faster random walk over online social networks by “rewiring ” the social network onthefly. Specifically, we develop Modified TOpology (MTO)Sampler which, by using only information exposed by the restrictive web interface, constructs a “virtual ” overlay topology of the social network while performing a random walk, and ensures that the random walk follows the modified overlay topology rather than the original one. We show that MTOSampler not only provably enhances the efficiency of sampling, but also achieves significant savings on query cost over realworld online social networks such as Google Plus, Epinion etc. I.
Learning Influence in Complex Social Networks
"... In open MultiAgent Systems, where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are a useful means of supporting cooperation in an emergent decentralised manner, however it takes time for ..."
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In open MultiAgent Systems, where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are a useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence. In this paper, we propose a general methodology for learning the network value of a node in terms of influence, and evaluate it using sampled realworld networks with a model of convention emergence that has realistic assumptions about the size of the convention space. We show that (i) the models resulting from our methodology are effective in predicting influential network locations, (ii) there are very few locations that can be classified as influential in typical networks, (iii) that four single metrics are robustly indicative of influence across a range of network structures, and (iv) our methodology learns which single metric or combined measure is the best predictor of influence in a given network.
Sampling social networks using shortest paths
"... 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 ..."
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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 MetropolisHastings random walk sampling in terms of relative error (RE), normalized root mean square error (NMSE), and KolmogorovSmirnov (KS) test.
On Random Walk Based Graph Sampling
, 2015
"... Random walk based graph sampling has been recognized as a fundamental technique to collect uniform node samples from a large graph. In this paper, we first present a comprehensive analysis of the drawbacks of three widelyused random walk based graph sampling algorithms, called reweighted random w ..."
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Random walk based graph sampling has been recognized as a fundamental technique to collect uniform node samples from a large graph. In this paper, we first present a comprehensive analysis of the drawbacks of three widelyused random walk based graph sampling algorithms, called reweighted random walk (RW) algorithm, MetropolisHastings random walk (MH) algorithm and maximumdegree random walk (MD) algorithm. Then, to address the limitations of these algorithms, we propose two general random walk based algorithms, named rejectioncontrolled MetropolisHastings (RCMH) algorithm and generalized maximumdegree random walk (GMD) algorithm. We show that RCMH balances the tradeoff between the limitations of RW and MH, and GMD balances the tradeoff between the drawbacks of RW and MD. To further improve the performance of our algorithms, we integrate the socalled delayed acceptance technique and the nonbacktracking random walk technique into RCMH and GMD respectively. We conduct extensive experiments over four realworld datasets, and the results demonstrate the effectiveness of the proposed algorithms.
Leveraging History for Faster Sampling of Online Social Networks
"... ABSTRACT With a vast amount of data available on online social networks, how to enable efficient analytics over such data has been an increasingly important research problem. Given the sheer size of such social networks, many existing studies resort to sampling techniques that draw random nodes fro ..."
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ABSTRACT With a vast amount of data available on online social networks, how to enable efficient analytics over such data has been an increasingly important research problem. Given the sheer size of such social networks, many existing studies resort to sampling techniques that draw random nodes from an online social network through its restrictive web/API interface. While these studies differ widely in analytics tasks supported and algorithmic design, almost all of them use the exact same underlying technique of random walk a Markov Chain Monte Carlo based method which iteratively transits from one node to its random neighbor. Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i.e., no access to the full graph topology). A problem with random walks, however, is the "burnin" period which requires a large number of transitions/queries before the sampling distribution converges to a stationary value that enables the drawing of samples in a statistically valid manner. In this paper, we consider a novel problem of speeding up the fundamental design of random walks (i.e., reducing the number of queries it requires) without changing the stationary distribution it achieves thereby enabling a more efficient "dropin" replacement for existing samplingbased analytics techniques over online social networks. Technically, our main idea is to leverage the history of random walks to construct a higherordered Markov chain. We develop two algorithms, Circulated Neighbors and Groupby Neighbors Random Walk (CNRW and GNRW) and rigidly prove that, no matter what the social network topology is, CNRW and GNRW offer better efficiency than baseline random walks while achieving the same stationary distribution. We demonstrate through extensive experiments on realworld social networks and synthetic graphs the superiority of our techniques over the existing ones.
Metric Convergence in Social Network Sampling
"... While enabling new research questions and methodologies, the massive size of social media platforms also poses a significant issue for the analysis of these networks. In order to deal with this data volume, researchers typically turn to samples of these graph structures to conduct their analysis. Th ..."
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While enabling new research questions and methodologies, the massive size of social media platforms also poses a significant issue for the analysis of these networks. In order to deal with this data volume, researchers typically turn to samples of these graph structures to conduct their analysis. This however raises the question about the representativeness of such limited crawls, and the amount of data necessary to come to stable predictions about the underlying systems. This paper analyzes the convergence of six commonly used topological metrics as a function of the crawling method and sample size used. We find that graph crawling methods drastically over and underestimate network metrics, and that a nontrivial amount of data is needed to arrive at a stable estimate of the underlying network. Categories and Subject Descriptors
Auton Agent MultiAgent Syst (2014) 28:836–866 DOI 10.1007/s1045801392411 Learning agent influence in MAS with complex social networks
, 2013
"... Abstract In complex open multiagent systems (MAS), where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are useful means of supporting cooperation in an emergent decentralised manner, howeve ..."
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Abstract In complex open multiagent systems (MAS), where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence. In this paper, we propose a methodology for learning a model for predicting the network value of an agent, in terms of the extent to which it can influence the rest of the population. Applying our methodology, we show that exploiting knowledge of the network structure can significantly increase the ability of individuals to influence which convention emerges. We evaluate our methodology in the context of two agentinteraction models, namely, the language coordination domain used by Salazar et al. (AI Communications 23(4): 357–372, 2010) and a coordination game of the form used by
Preprint submitted to International Journal of Communication Systems A new learning automata based sampling algorithm for social networks
"... Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access ..."
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Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information and social networks. In this paper, a sampling algorithm for complex social networks which is based on a new version of distributed learning automata (DLA) reported recently called extended distributed learning automata (eDLA) is proposed. For evaluation purpose, the eDLA based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for number of wellknown sampling algorithms in terms of relative error (RE) and KolmogorovSmirnov (KS) test. It is shown that eDLA based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA based sampling algorithm in comparison with the DLA based sampling algorithm has a 26.93 % improvement for the average of KS value for degree distribution taken over all test networks.
Physica A 396 (2014) 224–234 Contents lists available at ScienceDirect
"... journal homepage: www.elsevier.com/locate/physa Sampling from complex networks using distributed ..."
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journal homepage: www.elsevier.com/locate/physa Sampling from complex networks using distributed