Tree-Like Structure in Large Social and Information Networks

Adcock, Aaron; Sullivan, Blair D.; Mahoney, Michael W.

doi:10.1109/icdm.2013.77

Cited by 98 publications

(101 citation statements)

References 28 publications

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“…Multiple complex networks such as the Internet [28,14], data networks at the IP layer [24], and social and biological networks [4,2] show low δ-hyperbolicity (low hyperbolicity suggests a structure that is close to a tree structure [14,3]). Also, it has been observed that networks with this property have highly connected cores [24].…”

Section: Introductionmentioning

confidence: 99%

Core-Periphery Models for Graphs Based on their δ-Hyperbolicity: An Example Using Biological Networks

Alrasheed

Dragan

2015

Studies in Computational Intelligence

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Abstract. Hyperbolicity is a global property of graphs that measures how close their structures are to trees in terms of their distances. It embeds multiple properties that facilitate solving several problems that found to be hard in the general graph form. In this paper, we investigate the hyperbolicity of graphs not only by considering Gromov's notion of δ-hyperbolicity but also by analyzing its relationship to other graph's parameters. This new perspective allows us to classify graphs with respect to their hyperbolicity, and to show that many biological networks are hyperbolic. Then we introduce the eccentricity-based bending property which we exploit to identify the core vertices of a graph by proposing two models: the Maximum-Peak model and the Minimum Cover Set model.

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Section: Introductionmentioning

confidence: 99%

Core-Periphery Models for Graphs Based on their δ-Hyperbolicity: An Example Using Biological Networks

Alrasheed

Dragan

2015

Studies in Computational Intelligence

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“…Second, the mechanisms in [15], [16] are only efficient for social graphs with low treewidth property. Unfortunately, as reported in [19], the low tree width property does not hold in real world social graphs. We also made this observation in our real datasets.…”

Section: Related Workmentioning

confidence: 98%

Real time personalized search on social networks

Bao

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Abstract-Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.

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“…Consequently, one can think of all finite graphs as hyperbolic except that the value of decides how hyperbolic the graph is. On the other hand, as mentioned in Adcock et al, 7 when no finite exists (which may be the case for infinite graphs), the graph is considered nonhyperbolic.…”

Section: -Hyperbolicitymentioning

confidence: 99%

“…This forces the sides of the triangle to be curved toward its center as its size increases. 7 Multiple complex networks such as the Internet, 8,9 data networks at the IP layer, 6 and social and biological networks 10,11 show low -hyperbolicity (low hyperbolicity suggests a structure that is close to a tree structure 12 ). Also, it has been observed that networks with this property have highly connected cores.…”

Section: Introductionmentioning

confidence: 99%

Core–periphery models for graphs based on their δ-hyperbolicity: An example using biological networks

Alrasheed

Dragan

2016

Journal of Algorithms & Computational Technology

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Hyperbolicity is a global property of graphs that measures how close their structures are to trees in terms of their distances. It embeds multiple properties that facilitate solving several problems that found to be hard in the general graph form. In this paper, we investigate the hyperbolicity of graphs not only by considering Gromov's notion of -hyperbolicity but also by analyzing its relationship to other graph's parameters. This new perspective allows us to classify graphs with respect to their hyperbolicity and to show that many biological networks are hyperbolic. Then we introduce the eccentricity-based bending property which we exploit to identify the core vertices of a graph by proposing two models: the maximum-peak model and the minimum cover set model. In this extended version of the paper, we include some new theorems, as well as proofs of the theorems proposed in the conference paper. Also, we present the algorithms we used for each of the proposed core identification models, and we provide more analysis, explanations, and examples.

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Tree-Like Structure in Large Social and Information Networks

Cited by 98 publications

References 28 publications

Core-Periphery Models for Graphs Based on their δ-Hyperbolicity: An Example Using Biological Networks

Core-Periphery Models for Graphs Based on their δ-Hyperbolicity: An Example Using Biological Networks

Real time personalized search on social networks

Core–periphery models for graphs based on their δ-hyperbolicity: An example using biological networks

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