Aperiodically Intermittent Control for Synchronization on the Delayed Bipartite Networks With Non-Delay and Delay Couplings

Bipartite networks that characterize complex relationships among data arise in various domains. The existing bipartite network models are mainly based on a type of relationship between objects, and cannot effectively describe multiple relationships in the real world. In this paper, we propose a multi-relationship bipartite network (MBN) model, which can describe multiple relationships between two types of objects, and realizes simple weighted bipartite network reconstruction. Our model contains three major modules, namely multi-relationship bipartite network modeling (MBNM), multi-relationship aggregation module (MAM) and network reconstruction module (NRM). In MBNM, a multi-relationship bipartite network is proposed to describe multiple relationships between two types of objects. In the MAM, considering that different relationships have different information for the model, we introduce a novel relationship-level attention mechanism, and the aggregation of multiple relationships is carried out through the importance of each relationship. Based on the learning framework, the NRM can learn the potential representations of nodes after multi-relationship aggregation, and design a nonlinear fusion mechanism to reconstruct weighted bipartite network. We conducted extensive experiments on three real-world datasets and the results show that multi-relationship aggregation can effectively improve the performance of the model. In addition, experiments also show that our model can outperform existing competitive baseline method.

Outer synchronization of complex networks with internal delay and coupling delay via aperiodically intermittent pinning control

Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise to composition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding to cis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.

We study how the community structure of bipartite mutualistic networks changes in a dynamic context. First, we consider a real mutualistic network and introduce extinction events according to several scenarios. We model extinctions as node or interaction removals. For node removal, we consider random, directed and sequential extinctions; for interaction removal, we consider random extinctions. The bipartite network reorganizes showing an increase of the effective modularity and a fast decrease of the persistence of the species in the original communities with increasing number of extinction events. Second, we compare extinctions in a real mutualistic network with the growth of a bipartite network model. The modularity reaches a stationary value and nodes remain in the same community after joining the network. Our results show that perturbations and disruptive events affect the connectivity pattern of mutualistic networks at the mesoscale level. The increase of the effective modularity observed in some scenarios could provide some protection to the remaining ecosystem.

Many 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.

This paper aims at targeting some relevant issues for clustering tennis players and tournaments: (i) it considers players, tournaments and the relation between them; (ii) the relation is taken into account in the fuzzy clustering model based on the Partitioning Around Medoids (PAM) algorithm through spatial constraints; (iii) the attributes of the players and of the tournaments are of different nature, qualitative and quantitative. The proposal is novel for the methodology used, a spatial Fuzzy clustering model for players and for tournaments (based on related attributes), where the spatial penalty term in each clustering model depends on the relation between players and tournaments described in the adjacency matrix. The proposed model is compared with a bipartite players-tournament complex network model (the Degree-Corrected Stochastic Blockmodel) that considers only the relation between players and tournaments, described in the adjacency matrix, to obtain communities on each side of the bipartite network. An application on data taken from the ATP official website with regards to the draws of the tournaments, and from the sport statistics website Wheelo ratings for the performance data of players and tournaments, shows the performances of the proposed clustering model.

On designing decentralized impulsive controllers for synchronization of complex dynamical networks with nonidentical nodes and coupling delays

A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated. Furthermore, we propose three saturated adaptive pinning control protocols. The quasi-consensus is achieved through its own pinning as long as the agents remain connected to each other. Using the Lyapunov function method and inequality technique, the convergence range of the quasi-consensus error of a heterogeneous multi-agent system is obtained. Finally, the rationality of the proposed control protocol is verified through numerical simulation. Theoretical derivation and simulation results show that the novel proposed periodic intermittent adaptive control method with saturation can successfully be used to achieve the pinning of quasi-consensus of nonlinear heterogeneous multi-agent systems.

As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model to describe the risk propagation process during crises. We empirically test the model with 2007 US commercial banks balance sheet data and compare the model prediction of the failed banks with the real failed banks after 2007. We find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation. The results suggest that this model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008–2011.

Nonsingular Fixed-time Sliding Mode Control (NFSMC) method is one of the nonlinear control methods which is robust against uncertainties and external disturbances. This paper presents an NFSMC method to synchronize two similar Willamowski–Rossler reaction chaotic systems. The proposed control method is robust against uncertainties and external disturbances; also, it is an accurate and fast control method. The proposed control method is compared with a Nonsingular Terminal Sliding Mode Control. The comparison results show that the proposed control method provides a better control solution for this system.

Distribution characteristics of weighted bipartite evolving networks

Nowadays, due to advantages of maneuverability, portability and low cost, the mini quadrotor unmanned aerial vehicle (UAV) catches much attention. In some special applications such as narrow space search, stealth detection and cooperative detection in huge region, the mini quadrotor UAV is more suitable than a big one. However, light weight and mini size lead to phenomenon that it is hard for mini quadrotor UAV to use complex control method. Besides, the performance of wind resistant is significant for practicability. In this paper, the under-actuated dynamics, strong nonlinear and coupling characteristics has been analyzed. After that, we propose a three-dimensional fuzzy PID control method for stabilizing attitude control and precise trajectory tracking control. The error, derivative of the error and the second order derivative of the error are the three-dimensional inputs of the controller. After using Simulink, the result shows that the proposed control method realizes precise trajectory tracking and robust attitude control. Furthermore, a kind of turbulent wind field, generated based on Dryden model, is added to disturb the control process. This study shows that three-dimensional fuzzy control method is better than the conventional fuzzy PID method under impact of wind disturbance. Finally, the results prove that our proposed control method has both excellent static and dynamic characteristics.

Collaboration with content sharing via digital maps is a type of application that is characteristic of the context of the social web. A malicious activity that is di cult to detect in this interactive context is the generation of a false trend on the map as the result of a plot in which several false reports by more than one person are done. In this paper, we describe how modeling complex networks of crime reported on a collaborative (or crowd) map can help identify regularities, and therefore show deviations arising from malicious activity. The idea here is to model a network comprised of users who reported crimes and the locations where such crimes were reported (e.g.: a census tract). Starting from a bipartite network model in which the vertices are individuals and census tracts, we projected a monopartite network of users in which the edges indicate the strength of connection between them. This connection strength indicates the degree of co-relatedness of the reports of crime made by these two users in a particular place. By characterizing this, we were able to observe that the relationships of non-hub users among themselves are typically no stronger than the relationship between such non-hub users and the hubs. If this happens, the evidence of malicious activity becomes clear. Simulation of malicious activities in this dataset has allowed evaluating the contributions and limitations of our approach.

Disease-gene networks are bipartite networks that connect diseases to the genes with which they are associated. Due to the absence of tools to analyze bipartite networks, however, they are usually analyzed by "projecting" the bipartite network into a unipartite one by taking one side of the bipartition as the nodes and connecting two nodes if they share a common neighbor in the bipartite network. This projection, however, loses information, such as exactly which groups of diseases share a common gene.

Adaptive full-state constrained tracking control for mobile robotic system with unknown dead-zone input