Efficiently Estimating Motif Statistics of Large Networks

Wang, Pinghui; Lui, John C. S.; Ribeiro, Bruno; Towsley, Don; Zhao, Junzhou; Guan, Xiaohong

doi:10.1145/2629564

Cited by 90 publications

(140 citation statements)

References 32 publications

(30 reference statements)

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“…However, some of his topologies, namely, cellular, and core-periphery, differ from the ones examined in this paper. Other work delves into the properties of these subgraphs [39]. We endeavor to add to this line of research by examining the predictability of the complete networks' centrality measures and statistics from the analogous statistics for the sub-samples, all in the context of the three topologies.…”

Section: Network Samplingmentioning

confidence: 99%

Estimating Centrality Statistics for Complete and Sampled Networks: Some Approaches and Complications

Lee

2015

2015 48th Hawaii International Conference on System Sciences

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The study of large, "big data" networks is becoming increasingly common and relevant to our understanding of human systems. Many of the studied networks are drawn from social media and other web-based sources. As such, in-depth analysis of these dynamic structures e.g. in the context of cybersecurity, remains especially challenging. Due to the time and resources incurred in computing network measures for large networks, it is practical to approximate these whenever possible. We present some approximation techniques exploiting any tractable relationship between the measures and network characteristics such as size and density. We find there exist distinct functional relationships between network statistics of complex "slow" measures and "fast" measures, such as the linkage between betweenness centrality and network density. We also track how these relationships scale with network size. Specifically, we explore the efficacy of both linear modeling (i.e., correlations and least squares regression) and non-linear modeling in estimating the network measures of interest. We find that sparse, but not severely sparse, networks which admit sufficient entropy incur the most variance in the network statistics and, hence, more error in the estimation. We review our approaches with three prominent network topologies: random (aka Erdős-Rényi), Watts-Strogatz small-world, and scale-free networks. Finally, we assess how well the estimation approaches perform for sub-sampled networks.

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Section: Network Samplingmentioning

confidence: 99%

Estimating Centrality Statistics for Complete and Sampled Networks: Some Approaches and Complications

Lee

2015

2015 48th Hawaii International Conference on System Sciences

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“…There also exist works that mine frequent subgraphs from a single input graph. Our work is related to these works as our proposed method samples subgraphs from a single graph, which is chosen uniformly from the graph database.…”

Section: Related Workmentioning

confidence: 99%

FS³: A sampling based method for top‐k frequent subgraph mining

Saha

Hasan

2015

Statistical Analysis

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Abstract-Mining labeled subgraph is a popular research task in data mining because of its potential application in many different scientific domains. All the existing methods for this task explicitly or implicitly solve the subgraph isomorphism task which is computationally expensive, so they suffer from the lack of scalability problem when the graphs in the input database are large. In this work, we propose FS 3 , which is a sampling based method. It mines a small collection of subgraphs that are most frequent in the probabilistic sense. FS 3 performs a Markov Chain Monte Carlo (MCMC) sampling over the space of a fixed-size subgraphs such that the potentially frequent subgraphs are sampled more often. Besides, FS 3 is equipped with an innovative queue manager. It stores the sampled subgraph in a finite queue over the course of mining in such a manner that the top-k positions in the queue contain the most frequent subgraphs. Our experiments on database of large graphs show that FS 3 is efficient, and it obtains subgraphs that are the most frequent amongst the subgraphs of a given size.

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“…There also exist works [28][29][30][31][32][33] that mine frequent subgraphs from a single input graph. Our work is related to these works as our proposed method samples subgraphs from a single graph, which is chosen uniformly from the graph database.…”

Section: Related Workmentioning

confidence: 99%

FS<sup>3</sup>: A sampling based method for top-k frequent subgraph mining

Saha

Hasan

2014

2014 IEEE International Conference on Big Data (Big Data)

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Mining labeled subgraph is a popular research task in data mining because of its potential application in many different scientific domains. All the existing methods for this task explicitly or implicitly solve the subgraph isomorphism task which is computationally expensive, so they suffer from the lack of scalability problem when the graphs in the input database are large. In this work, we propose FS 3 , which is a sampling based method. It mines a small collection of subgraphs that are most frequent in the probabilistic sense. FS 3 performs a Markov Chain Monte Carlo (MCMC) sampling over the space of a fixed-size subgraphs such that the potentially frequent subgraphs are sampled more often. Besides, FS 3 is equipped with an innovative queue manager. It stores the sampled subgraph in a finite queue over the course of mining in such a manner that the top-k positions in the queue contain the most frequent subgraphs. Our experiments on database of large graphs show that FS 3 is efficient, and it obtains subgraphs that are the most frequent amongst the subgraphs of a given size.1 gSpan is the most polular among the existing graph mining methods. We use the Linux binary made available by the inventors:

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Efficiently Estimating Motif Statistics of Large Networks

Cited by 90 publications

References 32 publications

Estimating Centrality Statistics for Complete and Sampled Networks: Some Approaches and Complications

Estimating Centrality Statistics for Complete and Sampled Networks: Some Approaches and Complications

FS³: A sampling based method for top‐k frequent subgraph mining

FS<sup>3</sup>: A sampling based method for top-k frequent subgraph mining

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Efficiently Estimating Motif Statistics of Large Networks

Cited by 90 publications

References 32 publications

Estimating Centrality Statistics for Complete and Sampled Networks: Some Approaches and Complications

Estimating Centrality Statistics for Complete and Sampled Networks: Some Approaches and Complications

FS3: A sampling based method for top‐k frequent subgraph mining

FS<sup>3</sup>: A sampling based method for top-k frequent subgraph mining

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Product

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FS³: A sampling based method for top‐k frequent subgraph mining