bipartite networks
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2022 ◽  
Vol 16 (1) ◽  
Sarah Ouadah ◽  
Pierre Latouche ◽  
Stéphane Robin

Diversity ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 604
Kai-Qin Li ◽  
Zong-Xin Ren ◽  
Qiang Li

Background: Flowers are one of the important microhabitats promoting beetle diversity, but little is known about variation in the diversity of these insects at higher elevations. We do not know how divergent habitats influence the distribution of beetles among montane flora. Methods: We sampled beetles systematically in angiosperm flowers at 12 sites at two elevations (2700 m and 3200 m) and in two habitats (meadows and forests) for two consecutive years (2018 and 2019) on the Yulong Snow Mountain in Yunnan, southwestern China. Beetle diversity among sites were compared. Their interactions with flowers of identified plant species were analyzed using bipartite networks approach. Results: We collected 153 species of beetles from 90 plant species recording 3391 interactions. While plant species richness was lower at the higher, 3200 m elevation regardless of habitat type, beetle species richness was not significantly different among sites. Plant-beetle interaction networks were strongly modular and specialized. The structure of networks showed greater differences between elevations than between habitats. The turnover of networks was determined by species composition showing a weak influence by interaction rewiring. Conclusion: Our study showed a high diversity of beetles in flowers at higher elevations within this mountain complex. The role of beetles in plant–insect interactions within some sections of temperate, montane sites appear to be underestimated and warrant further study.

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Bader Fahad Alkhamees ◽  
Mogeeb A. A. Mosleh ◽  
Hussain AlSalman ◽  
Muhammad Azeem Akbar

The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimization (HHO) algorithm. Moreover, a new evaluation metric, i.e., h-Tversky Index (h-TI), is also proposed for computing node similarity and fitness is newly devised considering modularity. The goal of modularity is to quantify the goodness of a specific division of network to evaluate the accuracy of the proposed community detection. The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). The performance was analyzed for 250 iterations of the simulation experiments. Experimental results have shown that the proposed method demonstrated a maximal fitness of 0.74353 and maximal modularity of 0.77433, outperforming the state-of-the-art approaches, including h-index-based link prediction, such as Multiagent Genetic Algorithm (MAGA), Genetic Algorithm (GA), Memetic Algorithm for Community Detection in Bipartite Networks (MATMCD-BN), and HHO.

2021 ◽  
Vol 24 (4) ◽  
Simonetta Abenda

AbstractMaximal minors of Kasteleyn sign matrices on planar bipartite graphs in the disk count dimer configurations with prescribed boundary conditions, and the weighted version of such matrices provides a natural parametrization of the totally non–negative part of real Grassmannians (Postnikov et al. J. Algebr. Combin. 30(2), 173–191, 2009; Lam J. Lond. Math. Soc. (2) 92(3), 633–656, 2015; Lam 2016; Speyer 2016; Affolter et al. 2019). In this paper we provide a geometric interpretation of such variant of Kasteleyn theorem: a signature is Kasteleyn if and only if it is geometric in the sense of Abenda and Grinevich (2019). We apply this geometric characterization to explicitly solve the associated system of relations and provide a new proof that the parametrization of positroid cells induced by Kasteleyn weighted matrices coincides with that of Postnikov boundary measurement map. Finally we use Kasteleyn system of relations to associate algebraic geometric data to KP multi-soliton solutions. Indeed the KP wave function solves such system of relations at the nodes of the spectral curve if the dual graph of the latter represents the soliton data. Therefore the construction of the divisor is automatically invariant, and finally it coincides with that in Abenda and Grinevich (Sel. Math. New Ser. 25(3), 43, 2019; Abenda and Grinevich 2020) for the present class of graphs.

2021 ◽  
Vol 151 ◽  
pp. 111207
Shuhong Huang ◽  
Xiangrong Wang ◽  
Liyang Peng ◽  
Jiarong Xie ◽  
Jiachen Sun ◽  

2021 ◽  
Vol 6 (1) ◽  
Maksim Koptelov ◽  
Albrecht Zimmermann ◽  
Bruno Crémilleux ◽  
Lina F. Soualmia

AbstractMany aspects from real life with bi-relational structure can be modeled as bipartite networks. This modeling allows the use of some standard solutions for prediction and/or recommendation of new relations between objects in such networks. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in this type of networks. The motivation of this work stems from the importance of an application task, drug–target interaction prediction. Searching valid drug candidates for a given biological target is an essential part of modern drug development. We model the problem as link prediction in a bipartite multi-layer network, which helps to aggregate different sources of information into one single structure and as a result improves the quality of link prediction. We adapt existing community measures for link prediction to the case of bipartite multi-layer networks, propose alternative ways for exploiting communities, and show experimentally that our approach is competitive with the state-of-the-art. We also demonstrate the scalability of our approach and assess interpretability. Additional evaluations on data of a different origin than drug–target interactions demonstrate the genericness of the proposed approach.

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