A hypergraph model for representing scientific output

Lung, Rodica Ioana; Gaskó, Noémi; Suciu, Mihai Alexandru

doi:10.1007/s11192-018-2908-2

Cited by 20 publications

(14 citation statements)

References 34 publications

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“…It is also worth noting that our research confirms, in line with previous research 20 , 52 , that the alternative representation of collaboration networks, where hyperlinks connect publications instead of coauthors, yield a better representation in that for these type of collaboration networks all topological features eventually mature as layers are coalesced, whilst in the classical representation some topological feature never mature.…”

Section: Discussionsupporting

confidence: 90%

“…The outstanding importance of science and progress for the wellbeing of modern human societies, together with the consistent definition of scientific collaboration that is accurately documented in published research 5 , has given rise to a rich plethora of research dedicated to the determination of structure and function of scientific collaboration networks 6 – 12 . Along the same lines, citation networks 13 – 15 , bipartite author-publication networks 16 – 19 , hypergraphs of scientific output 20 , as well as simplicial descriptions of publications and corresponding topological methods 21 , 22 , have also been considered and studied in much detail.…”

Section: Introductionmentioning

confidence: 99%

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Multilayer representation of collaboration networks with higher-order interactions

Vasilyeva

Козлов

Alfaro-Bittner

et al. 2021

Sci Rep

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Collaboration patterns offer important insights into how scientific breakthroughs and innovations emerge in small and large research groups. However, links in traditional networks account only for pairwise interactions, thus making the framework best suited for the description of two-person collaborations, but not for collaborations in larger groups. We therefore study higher-order scientific collaboration networks where a single link can connect more than two individuals, which is a natural description of collaborations entailing three or more people. We also consider different layers of these networks depending on the total number of collaborators, from one upwards. By doing so, we obtain novel microscopic insights into the representativeness of researchers within different teams and their links with others. In particular, we can follow the maturation process of the main topological features of collaboration networks, as we consider the sequence of graphs obtained by progressively merging collaborations from smaller to bigger sizes starting from the single-author ones. We also perform the same analysis by using publications instead of researchers as network nodes, obtaining qualitatively the same insights and thus confirming their robustness. We use data from the arXiv to obtain results specific to the fields of physics, mathematics, and computer science, as well as to the entire coverage of research fields in the database.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Multilayer representation of collaboration networks with higher-order interactions

Vasilyeva

Козлов

Alfaro-Bittner

et al. 2021

Sci Rep

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“…A hypergraph model illustrates co-authorship data in a simple and elegant manner. In [13], authors have proposed the use of a crisp hypergraph model to represent publication data by considering papers as hypergraph nodes. Hyperedges, connecting the nodes, represent the authors connecting all their papers.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The strength of the collaboration is represented by the number of nodes in the intersecting hyperedges. A hypergraph model of co-authorship network represented in [13] is given in Fig. 1.47.…”

Section: Comparative Analysismentioning

confidence: 99%

Fuzzy Hypergraphs

Akram¹,

Luqman²

2020

Fuzzy Hypergraphs and Related Extensions

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In this chapter, we present fundamental and technical concepts like fuzzy hypergraphs, fuzzy column hypergraphs, fuzzy row hypergraphs, fuzzy competition hypergraphs, fuzzy k-competition hypergraphs, fuzzy neighborhood hypergraphs, and N -hypergraphs. We describe applications of fuzzy competition hypergraphs in decision support systems, including predator-prey relations in ecological niches, social networks, and business marketing. Further, we introduce complex fuzzy hypergraphs, μe iθ −level hypergraphs, covering constructions, 2-sections, and L 2 -sections of these hypergraphs. We define certain new products, including Cartesian product, minimal rank preserving, and maximal rank preserving direct products on complex fuzzy hypergraphs. Moreover, we present an application of our proposed model to illustrate the publication data in co-authorship networks. This chapter is basically due to [3, 7-9, 12, 25].

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“…These systems can be more precisely modeled using hypergraphs where nodes represent the interacting components, and hyperedges capture higher-order interactions (Bretto and et al 2013;Klamt et al 2009;Satchidanand et al 2014;Lung et al 2018). A hyperedge can capture a multi-way relation; for example, in a co-authorship network, where nodes represent authors, a hyperedge could represent a group of authors who collaborated for a common paper.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph clustering by iteratively reweighted modularity maximization

et al. 2020

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Learning on graphs is a subject of great interest due to the abundance of relational data from real-world systems. Many of these systems involve higher-order interactions (super-dyadic) rather than mere pairwise (dyadic) relationships; examples of these are co-authorship, co-citation, and metabolic reaction networks. Such super-dyadic relations are more adequately modeled using hypergraphs rather than graphs. Learning on hypergraphs has thus been garnering increased attention with potential applications in network analysis, VLSI design, and computer vision, among others. Especially, hypergraph clustering is gaining attention because of its enormous applications such as component placement in VLSI, group discovery in bibliographic systems, image segmentation in CV, etc. For the problem of clustering on graphs, modularity maximization has been known to work well in the pairwise setting. Our primary contribution in this article is to provide a generalization of the modularity maximization framework for clustering on hypergraphs. In doing so, we introduce a null model for graphs generated by hypergraph reduction and prove its equivalence to the configuration model for undirected graphs. The proposed graph reduction technique preserves the node degree sequence from the original hypergraph. The modularity function can be defined on a thus reduced graph, which can be maximized using any standard modularity maximization method, such as the Louvain method. We additionally propose an iterative technique that provides refinement over the obtained clusters. We demonstrate both the efficacy and efficiency of our methods on several real-world datasets.

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A hypergraph model for representing scientific output

Cited by 20 publications

References 34 publications

Multilayer representation of collaboration networks with higher-order interactions

Multilayer representation of collaboration networks with higher-order interactions

Fuzzy Hypergraphs

Hypergraph clustering by iteratively reweighted modularity maximization

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