Given a heterogeneous multilayer network with various connections in pharmacology, how can we detect components with intensive interactions and strong dependencies? Can we accurately capture ...suspicious groups in a multi-lot transaction network under camouflage? These challenges related to dense subgraph detection have been extensively studied in simple graphs (such as bipartite graph, multi-view network) but remain under-explored on complex networks. Existing methods struggle to effectively handle the intricate dependencies , let alone accurately identify the interrelated dense connected patterns within a series of complex heterogeneous networks. In this paper, we propose InDuen , a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks through joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. It is (a) effective for both large synthetic and real networks, (b) resistant to camouflage for anomaly detection, and (c) linearly scalable. Experimental results demonstrate that InDuen outperforms the state-of-the-art baselines in accurately detecting interrelated densest subgraphs under various settings. Furthermore, InDuen uncovers some intriguing patterns in real-world data, i.e., closely cooperated academic groups and interrelated dependent functional components in biology-net. InDuen achieves more than <inline-formula><tex-math notation="LaTeX">35 \times</tex-math></inline-formula> speedup compared to the SOTA method Destine .
Post-translational modifications (PTMs) are pivotal in controlling protein function, signaling pathways, and cellular processes, underscoring their importance in biological systems. PTMs not only ...regulate various signaling pathways by modifying individual residues but also regulate signaling pathways through the interaction of different modified residues within proteins or between proteins, which is known as PTM cross-talk. An in-depth study of the interactions between PTMs can lead to a clearer understanding of the regulatory mechanisms mediated by PTMs. Therefore, accurately identifying potential PTM cross-talk within proteins (Intra PTM cross-talk) or between proteins (Inter PTM cross-talk) is of utmost importance in biological research. In this work, we introduce an innovative approach called WPTCMN/PTCMN for simultaneous prediction of Intra/Inter PTM cross-talk using an integrated deep neural network, which is based on a Multilayer Network. Comprehensive experimental analysis demonstrates that using the Multilayer Network to capture the complex associations between Intra/Inter PTM cross-talk exhibits remarkable superiority in predicting PTM cross-talk. Specifically, the AUC value achieved on Intra PTM cross-talk is 0.924, while on Inter PTM cross-talk it reaches 0.872, surpassing existing methods. Therefore, WPTCMN/PTCMN represents an effective tool for simultaneous prediction of Intra/Inter PTM cross-talk.
•Propose an integrated deep neural network to predict intra and inter PTM cross-talk.•Employ a Multilayer Network to represent intricate associations between cross-talks.•Utilize random walks to dynamically learn the single-layer network features.•Consider protein evolutionary features, structural features, and dynamic features.•Design a feature selection method by using Sequential Forward Selection.
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The intensification of the use of different renewable energy sources is essential for the fulfillment of the Paris Agreement or for achieving the goals of sustainable development. The environmental ...impacts of various renewable energy sources have been engaging environmental professionals since their inception. The aim of this work is to collect the most important sizing variables and environmental impacts of hydropower, wind energy, geothermal energy, solar energy and biomass, which have been discussed in the literature.
By using the tools of network science, it is possible to jointly manage the environmental impacts and the sizing variables of renewable energy power plants, so the impact mitigation can be performed efficiently already in the design phase. From the sizing variables and the environmental impacts, a multi-layered network is formed, based on which the relationships between the impacts can be explored and more efficient mitigation solutions can be implemented.
The results show that the impacts of wind turbines on flying animals are outstanding, hydropower plants can be mostly described by changing the flow conditions, the noise and hydrothermal disturbance of geothermal power plants are outstanding, the visual and soil effects of solar power plants are most significant, while the biomass plants impacts related the harvest are most pronounced.
This work helps to understand the environmental impacts of the increased utilization of renewable energy sources better and provides a framework for practitioners to enforce environmental considerations in design processes more easily.
•A network-based environmental impact assessment method is developed.•298 sizing variables of renewable energy power plants are collected and connected.•117 environmental impacts are investigated.•The environmental impacts are linked to the sizing variables.•The co-occurrences of environmental impacts are highlighted.
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Multilayer Network (MN) is a complex network comprising a set of graphs (also referred to as layers) interconnected by edges or interlayer edges (or inter-edges) linking the nodes of different ...layers. In biology, a MN may model interactions among diseases, genes, and drugs, only using its structure. Recently, there has been a growing interest in comparing two MNs by revealing local regions of similarity as a counterpart of Network Alignment algorithms (NA) for simple networks. However, classical algorithms for NA such as Local NA (LNA) cannot be applied on multilayer networks, since they are not able to deal with interlayer edges. Therefore, there is a need for the introduction of novel algorithms. In this paper, we present MuLan, an algorithm for the local alignment of multilayer networks. MuLan is based on the building of a multilayer alignment graph starting from a set of seed nodes. Then it analyses such a graph by revealing conserved regions. We first show as proof of concept the performances of MuLaN on a set of synthetic multilayer networks. Then, we used as a case study a real multilayer network in the biomedical domain. Our results show that MuLaN can build high-quality alignments and can extract knowledge about the aligned multilayer networks. MuLaN is available at https://github.com/pietrocinaglia/mulan.
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Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, ...known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected ‘layers’ of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.
•We review the mathematical formalism of multilayer networks for animal behaviour.•It allows the investigation of new research questions in animal social behaviour.•It can offer novel behavioural insights at individual, group and population levels.•We give examples that implement multilayer methods in the study of animal sociality.
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Network ecology in dynamic landscapes Fortin, Marie-Josée; Dale, Mark R T; Brimacombe, Chris
Proceedings - Royal Society. Biological sciences/Proceedings - Royal Society. Biological Sciences,
04/2021, Volume:
288, Issue:
1949
Journal Article
Peer reviewed
Open access
Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to ...environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the 'ecological network dynamics framework'. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.
The integrated use of dockless bike-sharing (DBS) and metro has greatly promoted the development of multimodal transportations. It is challenging to understand the travel mobilities of DBS and metro ...due to their complicated flow structures. In this paper, we propose an interconnected double-layer network to analyze the mobilities, in which each travel mode constructs a layer network. The studied area is divided into grids with same size. The grids are considered as nodes and trips between nodes are considered as edges. The edges between different layers are transfer trips that are found by space-time constraints. The results show that the metro trip network is connected more tightly with high efficiency than DBS trip network. Moreover, the Gini coefficient of aggregate network is more than 0.9, which is larger than metro trip network and DBS trip network. It is observed that the transfer ratios between layers are high in morning peak, but low in evening peak. The multiplex participation coefficient is adopted to measure the disparity of degree distributions of a node in different layers. It is found that passengers are distributed more evenly in the central area than suburbs between two layers. Our findings could be helpful in planning and managing multimodal transportations.
•Building a multilayer network to analyze the integrated use of dockless bike-sharing and metro.•The metro trip network is connected mote tightly with high efficiency than DBS trip network.•The metro trip network, DBS trip network and aggregate network show ‘small world’ characteristics.•The Gini coefficient of aggregate network is more than 0.9, which is larger than metro trip network and DBS trip network.
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The water-energy-land nexus requires long-sighted approaches that help avoid maladaptive pathways to ensure its promise to deliver insights and tools that improve policy-making. Climate services can ...form the foundation to avoid myopia in nexus studies by providing information about how climate change will alter the balance of nexus resources and the nature of their interactions. Nexus studies can help climate services by providing information about the implications of climate-informed decisions for other economic sectors across nexus resources. First-of-its-kind guidance is provided to combine nexus studies and climate services. The guidance consists of ten principles and a visual guide, which are discussed together with questions to compare diverse case studies and with examples to support the application of the principles.
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•First-of-its-kind guidance on combining climate services with the water-energy-land nexus•New definition of the nexus based on networks and complexity science•A set of 10 guiding principles for local to regional cross-sectoral integrated assessment•Guiding questions to compare nexus case studies
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The emotion varies and propagates with the spatial and temporal information of individuals through social media, which uncovers several interaction mechanisms and features the community structure in ...order to facilitate individuals’ communication and emotional contagion in social networks. Aiming to show the detailed process and characteristics of emotional contagion within social media, we propose an emotional independent cascade model in which individual emotion can affect the subsequent emotion of his/her friends. The transmissibility is introduced to measure the capability of propagating emotion with respect to an individual in social networks. By analyzing the patterns of emotional contagion on Twitter data, we find that the value of transmissibility differs on different layers and on different community structures. Extensive experiments were conducted and the results reveal that, the polar emotion of hub users can lead to the disappearance of opposite emotion, and the transmissibility makes no sense. The final emotional distribution depends on the initial emotional distribution and the transmissibilities. Individuals from a small community are more likely to change their mood by the influence of community leaders. In addition, we compared the proposed model with two other models, the emotion-based spreader–ignorant–stifler model and the standard independent cascade model. The results demonstrate that the proposed model can reflect the real-world situation of emotional contagion for heterogeneous social media while the computational complexities of all these three models are similar.
•The transmissibility is introduced to measure the capability of spreading emotion.•A model is proposed to describe the spatio-temporal features of emotion contagion.•The transmissibility changes with interactions types and community structures.•The proposed model shows better performance than other models of emotion contagion.•The simulation results of our model reveal some interesting characteristics of social media.
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•Four novel multilayer network embedding algorithms are proposed with the consideration on four network structural properties.•Four objective functions based on NMF are specially designed and ...optimized to preserve the target structural properties.•Extensive experiments are conducted to evaluate the performances of the proposed embedding algorithms on different tasks.
Multilayer network is a structure commonly used to describe and model the complex interaction between sets of entities/nodes. A three-layer example is the author-paper-word structure in which authors are linked by co-author relation, papers are linked by citation relation, and words are linked by semantic relation. Network embedding, which aims to project the nodes in the network into a relatively low-dimensional space for latent factor analysis, has recently emerged as an effective method for a variety of network-based tasks, such as collaborative filtering and link prediction. However, existing studies of network embedding both focus on the single-layer network and overlook the structural properties of the network, e.g., the degree distribution and communities, which are significant for node characterization, such as the preferences of users in a social network. In this paper, we propose four multilayer network embedding algorithms based on Nonnegative Matrix Factorization (NMF) with consideration given to four structural properties: whole network (NNMF), community (CNMF), degree distribution (DNMF), and max spanning tree (TNMF). Experiments on synthetic data show that the proposed algorithms are able to preserve the desired structural properties as designed. Experiments on real-world data show that multilayer network embedding improves the accuracy of document clustering and recommendation, and the four embedding algorithms corresponding to the four structural properties demonstrate the differences in performance on these two tasks. These results can be directly used in document clustering and recommendation systems.
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