Metropolis Algorithms for Representative Subgraph Sampling

Hübler, Clemens; Kriegel, Hans‐Peter; Borgwardt, Karsten; Ghahramani, Zoubin

doi:10.1109/icdm.2008.124

Cited by 116 publications

(86 citation statements)

References 14 publications

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“…Another goal in network sampling has been to collect representative subnetworks from a population graph [10], [13], [15]. In this context, a subnetwork that matches many popular topological characteristics of the population graph is considered representative.…”

Section: Related Workmentioning

confidence: 99%

Impact of sampling design in estimation of graph characteristics

Cem

Tozal

Sarac

2013

2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)

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Abstract-Studying structural and functional characteristics of large scale graphs (or networks) has been a challenging task due to the related computational overhead. Hence, most studies consult to sampling to gather necessary information to estimate various features of these big networks. On the other hand, using a best effort approach to graph sampling within the constraints of an application domain may not always produce accurate estimates. In fact, the mismatch between the characteristics of interest and the utilized network sampling methodology may result in incorrect inferences about the studied characteristics of the underlying system. In this study we empirically investigate the sources of information loss in a sampling process; identify the fundamental factors that need to be carefully considered in a sampling design; and use several synthetic and real world graphs to elaborately demonstrate the mismatch between the sampling design and graph characteristics of interest.

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Section: Related Workmentioning

confidence: 99%

Impact of sampling design in estimation of graph characteristics

Cem

Tozal

Sarac

2013

2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)

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“…In Section 4.2, we evaluate the performance of several of these algorithms in the context of undirected social networks and in their ability to sample alternative notions of network centrality. Finally, also related to this work are the existing studies on representative subgraph sampling such as [13][14][15].…”

Section: Background and Related Workmentioning

confidence: 99%

Online Sampling of High Centrality Individuals in Social Networks

Maiya

Berger-Wolf

2010

Advances in Knowledge Discovery and Data Mining

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Abstract. In this work, we investigate the use of online or "crawling" algorithms to sample large social networks in order to determine the most influential or important individuals within the network (by varying definitions of network centrality). We describe a novel sampling technique based on concepts from expander graphs. We empirically evaluate this method in addition to other online sampling strategies on several realworld social networks. We find that, by sampling nodes to maximize the expansion of the sample, we are able to approximate the set of most influential individuals across multiple measures of centrality.

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“…We found another recent work [16] that uses Metropolis sampling in the domain of graph mining. The objective of their work is to obtain a model that finds subgraphs that approximate a given degree distribution.…”

Section: Related Workmentioning

confidence: 99%

Output space sampling for graph patterns

2009

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Recent interest in graph pattern mining has shifted from finding all frequent subgraphs to obtaining a small subset of frequent subgraphs that are representative, discriminative or significant. The main motivation behind that is to cope with the scalability problem that the graph mining algorithms suffer when mining databases of large graphs. Another motivation is to obtain a succinct output set that is informative and useful. In the same spirit, researchers also proposed sampling based algorithms that sample the output space of the frequent patterns to obtain representative subgraphs. In this work, we propose a generic sampling framework that is based on Metropolis-Hastings algorithm to sample the output space of frequent subgraphs. Our experiments on various sampling strategies show the versatility, utility and efficiency of the proposed sampling approach.

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Metropolis Algorithms for Representative Subgraph Sampling

Abstract: Abstract

Cited by 116 publications

References 14 publications

Impact of sampling design in estimation of graph characteristics

Impact of sampling design in estimation of graph characteristics

Online Sampling of High Centrality Individuals in Social Networks

Output space sampling for graph patterns

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