Random hypergraphs and their applications

Ghoshal, Gourab; Zlatić, Vinko; Caldarelli, Guido; Newman, M. E. J.

doi:10.1103/physreve.79.066118

Cited by 244 publications

(183 citation statements)

References 17 publications

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“…In this paper, we use the tripartite hypergraph representation given by Ref. [21], where a hyperedge simply consists of one user, one resource and one tag. Fig.…”

Section: Modeling Tripartite Hypergraphsmentioning

confidence: 99%

A hypergraph model of social tagging networks

2010

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The past few years have witnessed the great success of a new family of paradigms, so-called folksonomy, which allows users to freely associate tags to resources and efficiently manage them. In order to uncover the underlying structures and user behaviors in folksonomy, in this paper, we propose an evolutionary hypergrah model to explain the emerging statistical properties. The present model introduces a novel mechanism that one can not only assign tags to resources, but also retrieve resources via collaborative tags. We then compare the model with a real-world dataset: Del.icio.us. Indeed, the present model shows considerable agreement with the empirical data in following aspects: power-law hyperdegree distributions, negtive correlation between clustering coefficients and hyperdegrees, and small average distances. Furthermore, the model indicates that most tagging behaviors are motivated by labeling tags to resources, and tags play a significant role in effectively retrieving interesting resources and making acquaintance with congenial friends. The proposed model may shed some light on the in-depth understanding of the structure and function of folksonomy.

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“…In this paper, we use the tripartite hypergraph representation given by Ref. [21], where a hyperedge simply consists of one user, one resource and one tag. Fig.…”

Section: Modeling Tripartite Hypergraphsmentioning

confidence: 99%

A hypergraph model of social tagging networks

2010

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“…One such application is a folksonomy, a social network in which users can annotate items with different tags [11]. In this application, the graph consists of nodes corresponding to different users, different items, and different tags.…”

Section: Introductionmentioning

confidence: 99%

Community recovery in hypergraphs

Ahn

Lee

Suh

2016

2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Community recovery is a central problem that arises in a wide variety of applications such as network clustering, motion segmentation, face clustering and protein complex detection. The objective of the problem is to cluster data points into distinct communities based on a set of measurements, each of which is associated with the values of a certain number of data points. While most of the prior works focus on a setting in which the number of data points involved in a measurement is two, this work explores a generalized setting in which the number can be more than two. Motivated by applications particularly in machine learning and channel coding, we consider two types of measurements: (1) homogeneity measurement which indicates whether or not the associated data points belong to the same community; (2) parity measurement which denotes the modulo-2 sum of the values of the data points. Such measurements are possibly corrupted by Bernoulli noise. We characterize the fundamental limits on the number of measurements required to reconstruct the communities for the considered models.

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“…In addition, the two pure algorithms might reduce some information of social tagging networks. The hypergraph theory [Ghoshal et al, 2009;] is expected to harness the whole network structure without losing any information and thus provide a promising way to obtain better recommendation performance.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Hybrid Recommendation Algorithm Based on Two Roles of Social Tags

Zhang

Liu

2012

Int. J. Bifurcation Chaos

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The past few years have witnessed the great success of a new family of paradigms, social tagging networks, which allows users to freely associate social tags to items and efficiently manage them. Thus it provides us a promising way to effectively find useful and interesting information. In this paper, we consider two typical roles of social tags: (i) an accessorial tool helping users organize items; (ii) a bridge that connects users and items. We then propose a hybrid algorithm to integrate the two different roles to obtain better recommendation performance. Experimental results on a real-world data set, Del.icio.us, shows that it can significantly enhance both the algorithmic accuracy and diversity.

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Random hypergraphs and their applications

Cited by 244 publications

References 17 publications

A hypergraph model of social tagging networks

A hypergraph model of social tagging networks

Community recovery in hypergraphs

Hybrid Recommendation Algorithm Based on Two Roles of Social Tags

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