Multipartite Networks Research Articles

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

ABSTRACT Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins – namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD—were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

A new clustering method based on multipartite networks.

The clustering problem is one of the most studied and challenging in machine learning, as it attempts to identify similarities within data without any prior knowledge. Among modern clustering algorithms, the network-based ones are some of the most popular. Most of them convert the data into a graph in which instances of the data represent the nodes and a similarity measure is used to add edges. This article proposes a novel approach that uses a multipartite network in which layers correspond to attributes of the data and nodes represent intervals for the data. Clusters are intuitively constructed based on the information provided by the paths in the network. Numerical experiments performed on synthetic and real-world benchmarks are used to illustrate the performance of the approach. As a real application, the method is used to group countries based on health, nutrition, and population information from the World Bank database. The results indicate that the proposed method is comparable in performance with some of the state-of-the-art clustering methods, outperforming them for some data sets.

An analytic strategy for data processing of multimode networks

Complex network data structures are considered to capture the richness of social phenomena and real-life data settings. Multipartite networks are an example in which various scenarios are represented by different types of relations, actors, or modes. Within this context, the present contribution aims at discussing an analytic strategy for simplifying multipartite networks in which different sets of nodes are linked. By considering the connection of multimode networks and hypergraphs as theoretical concepts, a three-step procedure is introduced to simplify, normalize, and filter network data structures. Thus, a model-based approach is introduced for derived bipartite weighted networks in order to extract statistically significant links. The usefulness of the strategy is demonstrated in handling two application fields, that is, intranational student mobility in higher education and research collaboration in European framework programs. Finally, both examples are explored using community detection algorithms to determine the presence of groups by mixing up different modes.

Scaling migrations to communities: An empirical case of migration network in the Arctic

Seasonal migrants transport energy, nutrients, contaminants, parasites and diseases, while also connecting distant food webs between communities and ecosystems, which contributes to structuring meta-communities and meta-ecosystems. However, we currently lack a framework to characterize the structure of the spatial connections maintained by all migratory species reproducing or wintering in a given community. Here, we use a network approach to represent and characterize migratory pathways at the community level and provide an empirical description of this pattern from a High-Arctic terrestrial community. We define community migration networks as multipartite networks representing different biogeographic regions connected with a focal community through the seasonal movements of its migratory species. We focus on the Bylot Island High-Arctic terrestrial community, a summer breeding ground for several migratory species. We define the non-breeding range of each species using tracking devices, or range maps refined by flyways and habitat types. We show that the migratory species breeding on Bylot Island are found across hundreds of ecoregions on several continents during the non-breeding period and present a low spatial overlap. The migratory species are divided into groups associated with different sets of ecoregions. The non-random structure observed in our empirical community migration network suggests evolutionary and geographic constraints as well as ecological factors act to shape migrations at the community level. Overall, our study provides a simple and generalizable framework as a starting point to better integrate migrations at the community level. Our framework is a far-reaching tool that could be adapted to address the seasonal transport of energy, contaminants, parasites and diseases in ecosystems, as well as trophic interactions in communities with migratory species.

Matrix Tri-Factorization Over the Tropical Semiring

Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, or solving a decision problem. In previous studies, a matrix two-factorization algorithm based on the tropical semiring has been applied to investigate bipartite and tripartite networks. Tri-factorization algorithms based on standard linear algebra are used for solving tasks such as data fusion, co-clustering, matrix completion, community detection, and more. However, there is currently no tropical matrix tri-factorization approach, which would allow for the analysis of multipartite networks with a high number of parts. To address this, we propose the triFastSTMF algorithm, which performs tri-factorization over the tropical semiring. We apply it to analyze a four-partition network structure and recover the edge lengths of the network. We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network. When trained on a specific subnetwork and used to predict the whole network, triFastSTMF outperforms Fast-NMTF by several orders of magnitude smaller error. The robustness of triFastSTMF is due to tropical operations, which are less prone to predict large values compared to standard operations.

Approaches to blockmodeling dynamic networks: A Monte Carlo simulation study

Blockmodeling refers to a variety of statistical methods for reducing and simplifying large and complex networks. While methods for blockmodeling networks observed at one time point are well established, it is only recently that researchers have proposed several methods for analysing dynamic networks (i.e., networks observed at multiple time points). The considered approaches are based on k-means or stochastic blockmodeling, with different ways being used to model time dependency among time points. Their novelty means they have yet to be extensively compared and evaluated and the paper therefore aims to compare and evaluate them using Monte Carlo simulations. Different network characteristics are considered, including whether tie formation is random or governed by local network mechanisms. The results show the Dynamic Stochastic Blockmodel (Matias and Miele 2017) performs best if the blockmodel does not change; otherwise, the Stochastic Blockmodel for Multipartite Networks (Bar-Hen et al. 2020) does.

Limitations of nearest-neighbor quantum networks

Quantum communication research has in recent years shifted to include multipartite networks for which questions of quantum network routing naturally emerge. To understand the potential for multipartite routing, we focus on the most promising architectures for future quantum networks---those connecting nodes close to each other. Nearest-neighbor networks, such as rings, lines, and grids, have been studied under different communication scenarios to facilitate the sharing of quantum resources especially in the presence of bottlenecks. We analyze the potential of nearest-neighbor entangling gate quantum networks and identify some serious limitations by demonstrating that rings and lines cannot overcome common bottleneck communication problems.

Boosting house price predictions using geo-spatial network embedding

Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users’ interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the geo-spatial network embedding (GSNE), that learns the embeddings of houses and various types of points of interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model. Relevant source code for GSNE is available at: https://github.com/sarathismg/gsne .

On the Power of Randomization for Scheduling Real-Time Traffic in Wireless Networks

In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that can lead to poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to any traffic (arrival and deadline) process that evolves as an unknown positive recurrent Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants.

Subspace controllability of multi-partite spin networks

Abstract In a network of spin 1 2 particles, controlled through an external electro-magnetic field, the gyromagnetic ratio of each spin is a parameter that characterizes the interaction of the spin with the external control field. Multipartite networks are such that the spins are divided into subsets according to their gyromagnetic ratio and spins in one set interact in the same way with all spins in another set. Due to the presence of symmetries in these types of systems, the underlying Hilbert state space splits into invariant subspaces for the dynamics. Subspace controllability is verified if every unitary evolution can be generated by the dynamics on these subspaces. We give an exact characterization, in terms of graph theoretic conditions, of subspace controllability for multipartite quantum spin networks. This extends and unifies previous results.

Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks.

Many systems of scientific interest can be conceptualized as multipartite networks. Examples include the spread of sexually transmitted infections, scientific collaborations, human friendships, product recommendation systems, and metabolic networks. In practice, these systems are often studied after projection onto a single class of nodes, losing crucial information. Here, we address a significant knowledge gap by comparing transmission dynamics on temporal multipartite networks and on their time-aggregated unipartite projections to determine the impact of the lost information on our ability to predict the systems' dynamics. We show that the dynamics of transmission models can be dramatically dissimilar on multipartite networks and on their projections at three levels: final outcome, the magnitude of the variability from realization to realization, and overall shape of the temporal trajectory. We find that the ratio of the number of nodes to the number of active edges over the time-aggregation scale determines the ability of projected networks to capture the dynamics on the multipartite network. Finally, we explore which properties of a multipartite network are crucial in generating synthetic networks that better reproduce the dynamical behavior observed in real multipartite networks.

Optimal Wirelength of Balanced Complete Multipartite Graphs onto Cartesian Product of {Path, Cycle} and Trees

In any interconnection network, task allocation plays a major role in the processor speed as fair distribution leads to enhanced performance. Complete multipartite networks serve well for this purpose as the task can be split into different partites which improves the degree of reliability of the network. Such an allocation process in the network can be done by means of graph embedding. The optimal wirelength of a graph embedding helps in the distribution of deterministic algorithms from the guest graph to other host graphs in order to incorporate its unique deterministic properties on that chosen graph. In this paper, we propose an algorithm to compute the optimal wirelength of balanced complete multipartite graphs onto the Cartesian product of trees with path and cycle. Moreover, we derive the closed formulae for wirelengths in specific trees like (1-rooted) complete binary tree and sibling graphs.

Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Generalized multipartite networks consist in the joint observation of several networks implying some common pre-specified groups of individuals. Such complex networks arise commonly in social sciences, biology, ecology, etc. We propose a flexible probabilistic model named Multipartite Block Model (MBM) able to unravel the topology of multipartite networks by identifying clusters (blocks) of nodes sharing the same patterns of connectivity across the collection of networks they are involved in. The model parameters are estimated through a variational version of the Expectation–Maximization algorithm. The numbers of blocks are chosen using an Integrated Completed Likelihood criterion specifically designed for our model. A simulation study illustrates the robustness of the inference strategy. Finally, two datasets respectively issued from ecology and ethnobiology are analyzed with the MBM in order to illustrate its flexibility and its relevance for the analysis of real datasets. The inference procedure is implemented in an R -package GREMLIN , available on Github ( https://github.com/Demiperimetre/GREMLINhttps://github.com/Demiperimetre/GREMLIN ).

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine.

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

Breakup of Directed Multipartite Networks

A complex network in reality often consists of profuse components, which might suffer from unpredictable perturbations. Because the components of a network could be interdependent, therefore the failures of a few components may trigger catastrophes to the entire network. It is thus pivotal to exploit the robustness of complex networks. Existing studies on network robustness mainly deal with interdependent or multilayer networks; little work is done to investigate the robustness of multipartite networks, which are an indispensable part of complex networks. Here, we plumb the robustness of directed multipartite networks. To be specific, we exploit the robustness of bi-directed and unidirectional multipartite networks in face of random node failures. We, respectively, establish cascading and non-cascading models based on the largest connected component concept for depicting the dynamical processes on bi-directed and unidirectional multipartite networks subject to perturbations. Based on our developed models, we, respectively, derive the corresponding percolation theories for mathematically computing the robustness of directed multipartite networks subject to random node failures. We unravel the first-order and second-order phase transition phenomena on the robustness of directed multipartite networks. The correctness of our developed theories has been verified through experiments on computer-generated as well as real-world multipartite networks.

MiRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology

miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primarily on miRNA-target gene interactions, it has become clear that in order to obtain a global view of miRNA functions, it is necessary to bring other important players into the context during analysis. Driven by this concept, in miRNet version 2.0, we have (i) added support for transcription factors (TFs) and single nucleotide polymorphisms (SNPs) that affect miRNAs, miRNA-binding sites or target genes, whilst also greatly increased (>5-fold) the underlying knowledgebases of miRNAs, ncRNAs and disease associations; (ii) implemented new functions to allow creation and visual exploration of multipartite networks, with enhanced support for in situ functional analysis and (iii) revamped the web interface, optimized the workflow, and introduced microservices and web application programming interface (API) to sustain high-performance, real-time data analysis. The underlying R package is also released in tandem with version 2.0 to allow more flexible data analysis for R programmers. The miRNet 2.0 website is freely available at https://www.mirnet.ca.

Percolation Theories for Multipartite Networked Systems under Random Failures

Real-world complex systems inevitably suffer from perturbations. When some system components break down and trigger cascading failures on a system, the system will be out of control. In order to assess the tolerance of complex systems to perturbations, an effective way is to model a system as a network composed of nodes and edges and then carry out network robustness analysis. Percolation theories have proven as one of the most effective ways for assessing the robustness of complex systems. However, existing percolation theories are mainly for multilayer or interdependent networked systems, while little attention is paid to complex systems that are modeled as multipartite networks. This paper fills this void by establishing the percolation theories for multipartite networked systems under random failures. To achieve this goal, this paper first establishes two network models to describe how cascading failures propagate on multipartite networks subject to random node failures. Afterward, this paper adopts the largest connected component concept to quantify the networks’ robustness. Finally, this paper develops the corresponding percolation theories based on the developed network models. Simulations on computer-generated multipartite networks demonstrate that the proposed percolation theories coincide quite well with the simulations.

Metacommunity robustness of plant-fly-wasp tripartite networks with specialization to habitat loss.

Recent observations have found plant-species-specific fly-host selection (i.e., specialization) of wasp parasitoids (wasps) in plant-fly-wasp (P-F-W) tripartite networks, yet no study has explored the dynamical implications of such high-order specialization for the persistence of this network. Here we develop a patch-dynamic framework for a unique P-F-W tripartite network with specialization observed in eastern Tibetan Plateau and explore its metacommunity robustness to habitat loss. We show that specialization in parasitoidism promotes fly species diversity, while the richness of both plant and wasp decreases. Compared to other two null models, real network structure favors plant species coexistence but increases the extinction risk for both flies and wasps. However, these effects of specialization and network structure would be weakened and ultimately disappear with increasing habitat loss. Interestingly, intermediate levels of habitat loss can maximize the diversity of flies and wasps, while increasing or decreasing habitat loss results in more species losses, supporting intermediate disturbance hypothesis. Finally, we observe that high levels of habitat loss initiate a bottom-up cascade of species extinction from plants to both flies and wasps, resulting in a rapid collapse of the whole tripartite networks. Overall, this theoretical framework is the first attempt to characterize the dynamics of whole tripartite metacommunities interacting in realistic high-order ways, offering new insights into complex multipartite networks.

Robustness Evaluation of Multipartite Complex Networks Based on Percolation Theory

To investigate the robustness of complex networks in face of disturbances can help prevent potential network disasters. Percolation on networks is a potent instrument for network robustness analysis. However, existing percolation theories are primarily developed for interdependent or multilayer networks. Little attention is paid to multipartite networks which are an indispensable part of complex networks. In this article, we theoretically explore the robustness of multipartite networks under node failures. We put forward the generic percolation theory for gauging the robustness of multipartite networks with arbitrary degree distributions. Our developed theory is capable of quantifying the robustness of multipartite networks under either random or target node attacks. Our theory unravels the second order phase transition phenomenon for multipartite networks. In order to verify the correctness of the proposed theory, simulations on computer generated multipartite networks have been carried out. The experiments demonstrate that the simulation results coincide quite well with that yielded by the proposed theory.

On the Robustness of Complex Systems With Multipartitivity Structures Under Node Attacks

Complex systems in the real world inevitably suffer from unpredictable perturbations, which can trigger system disasters, wreaking significant economical losses. To exploit the robustness of complex systems in the face of disturbances is of great significance. One of the most useful methods for system robustness analysis comes from the field of complex networks characterized by percolation theories. Many percolation theories, therefore, have been developed by researchers to investigate the robustness of diverse complex networks. Nevertheless, extant percolation theories are primarily devised for multilayer or interdependent networks. Little endeavor is dedicated to systems with multipartitivity structures, that is, multipartite networks, which are an indispensable part of complex networks. This paper fills this research gap by theoretically examining the robustness of multipartite networks under random or target node attacks. The generic percolation theory for robustness analysis of multipartite networks is accordingly put forward. To validate the correctness of the proposed percolation theory, we carry out simulations on computer-generated multipartite networks with Poisson degree distributions. The results yielded by the proposed theory coincide well with the simulations. Both theoretical and simulation results suggest that complex systems with multipartitivity structures could be more robust than those with multilayer structures.