Hierarchical Network Model Research Articles

Predicting Traveler Movement Based on a Hybrid Model of Hierarchical Clustering and Bayesian Network

Predicting Traveler Movement Based on a Hybrid Model of Hierarchical Clustering and Bayesian Network

Learning Geographical Hierarchy Features via a Compositional Model

Image location prediction is used to estimate the geolocation where an image is taken, which is important for many image applications, such as image retrieval, image browsing, and organization. Since a social image contains heterogeneous contents, such as visual content and textual content, effectively incorporating these contents to predict location is nontrivial. Moreover, it is observed that image content patterns and the locations where they may appear correlate hierarchically. Traditional image location prediction methods mainly adopt a single-level architecture and assume images are independently distributed in geographical space, which is not directly adaptable to the hierarchical correlation. In this paper, we propose a geographically hierarchical bi-modal deep belief network (GH-BDBN) model, which is a compositional learning architecture that integrates multi-modal deep learning model with a non-parametric hierarchical prior model. GH-BDBN learns a joint representation capturing the correlations among different types of image content using a bi-modal DBN, with a geographically hierarchical prior over the joint representation to model the hierarchical correlation between image content and location. Then, an efficient inference algorithm is proposed to learn the parameters and the geographical hierarchical structure of geographical locations. Experimental results demonstrate the superiority of our model for image location prediction.

A Neural Model of Coordinated Head and Eye Movement Control

Gaze shifts require the coordinated movement of both the eyes and the head in both animals and humanoid robots. To achieve this the brain and the robot control system needs to be able to perform complex non-linear sensory-motor transformations between many degrees of freedom and resolve the redundancy in such a system. In this article we propose a hierarchical neural network model for performing 3-D coordinated gaze shifts. The network is based on the PC/BC-DIM (Predictive Coding/Biased Competition with Divisive Input Modulation) basis function model. The proposed model consists of independent eyes and head controlled circuits with mutual interactions for the appropriate adjustment of coordination behaviour. Based on the initial eyes and head positions the network resolves redundancies involved in 3-D gaze shifts and produces accurate gaze control without any kinematic analysis or imposing any constraints. Furthermore the behaviour of the proposed model is consistent with coordinated eye and head movements observed in primates.

Commentary: Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.

Commentary: Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.

Power to Detect Intervention Effects on Ensembles of Social Networks

The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention effect using the hierarchical latent space model, an important subfamily of HNMs. We derive basic convergence results and asymptotic bounds on power, showing that standard error for the treatment effect is inversely proportional to the product of the number of ties and the number of networks; a result rather different from the usual effect of cluster size in hierarchical linear models, for example. We explore these results with a simulation study and suggest a tentative approach to power for practical applications.

Explicit information for category-orthogonal object properties increases along the ventral stream.

Extensive research has revealed that the ventral visual stream hierarchically builds a robust representation for supporting visual object categorization tasks. We systematically explored the ability of multiple ventral visual areas to support a variety of 'category-orthogonal' object properties such as position, size and pose. For complex naturalistic stimuli, we found that the inferior temporal (IT) population encodes all measured category-orthogonal object properties, including those properties often considered to be low-level features (for example, position), more explicitly than earlier ventral stream areas. We also found that the IT population better predicts human performance patterns across properties. A hierarchical neural network model based on simple computational principles generates these same cross-area patterns of information. Taken together, our empirical results support the hypothesis that all behaviorally relevant object properties are extracted in concert up the ventral visual hierarchy, and our computational model explains how that hierarchy might be built.

A Biologically Inspired Model of Distributed Online Communication Supporting Efficient Search and Diffusion of Innovation

We inhabit a world that is not only small but supports efficient decentralized search - an individual using local information can establish a line of communication with another completely unknown individual. Here we augment a hierarchical social network model with communication between and within communities. We argue that organization into communities would decrease overall decentralized search times. We take inspiration from the biological immune system which organizes search for pathogens in a hybrid modular strategy. Our strategy has relevance in search for rare amounts of information in online social networks. Our work also has implications for design of efficient online networks that could have an impact on networks of human collaboration, scientific collaboration and networks used in targeted manhunts. Real world systems, like online social networks, have high associated delays for long-distance links, since they are built on top of physical networks. Such systems have been shown to densify. Hence such networks will have a communication cost due to space and the requirement of maintaining connections. We have incorporated such a non-spatial cost to communication. We introduce the notion of a community size that increases with the size of the system, which is shown to reduce the time to search for information in networks. Our final strategy balances search times and participation costs and is shown to decrease time to find information in decentralized search in online social networks. Our strategy also balances strong-ties and weak-ties over long distances and may ultimately lead to more productive and innovative networks of human communication and enterprise. We hope that this work will lay the foundation for strategies aimed at producing global scale human interaction networks that are sustainable and lead to a more networked, diverse and prosperous society.

Confidence-based integrated reweighting model of task-difficulty explains location-based specificity in perceptual learning.

Perceptual learning is classically thought to be highly specific to the trained stimuli's retinal locations. However, recent research using a novel double-training paradigm has found dramatic transfer of perceptual learning to untrained locations. These results challenged existing models of perceptual learning and provoked intense debate in the field. Recently, Hung and Seitz (2014) showed that previously reported results could be reconciled by considering the details of the training procedure, in particular, whether it involves prolonged training at threshold using a single staircase procedure or multiple staircases. Here, we examine a hierarchical neural network model of the visual pathway, built upon previously proposed integrated reweighting models of perceptual learning, to understand how retinotopic transfer depends on the training procedure adopted. We propose that the transfer and specificity of learning between retinal locations can be explained by considering the task-difficulty and confidence during training. In our model, difficult tasks lead to higher learning of weights from early visual cortex to the decision unit, and thus to specificity, while easy tasks lead to higher learning of weights from later stages of the visual hierarchy and thus to more transfer. To model interindividual difference in task-difficulty, we relate task-difficulty to the confidence of subjects. We show that our confidence-based reweighting model can account for the results of Hung and Seitz (2014) and makes testable predictions.

Is it right to estimate inter-modular connectivity from local field potentials?

Human brains with hundreds of billions of neurons are organized in a hierarchical modular network. There have been many attempts to estimate inter-modular connectivity utilizing coherent neuronal activities of a huge number of neurons, such as the electro-encephalogram, the magneto-encephalogram, and the functional magnetic resonance imaging. Here we ask a question: Is the inter-modular connectivity estimated from the modular activities consistent with the inter-modular connectivity that could be extracted from the network connectivity of individual nodes? To answer this question, we introduce a method of estimating the inter-modular connectivity based on the analysis of inverse phase synchronization [1,2]. For coupled phase oscillators on a hierarchical modular network shown in the Figure 1(A), the local field potential corresponding to a module is defined as the mean phase of oscillators belonging to a subset of the module. Figure 1 (A) A hierarchical modular network model 256-p-q-r, where p denotes the number of links of one node with nodes of its lower-level module, q links with nodes of the rest modules in its upper-level module, and r links with nodes of any modules from the ... For strong coupling strength, it is shown in Figure 1(B) that the inverse phase synchronization index grows linearly with the number of links connecting two modules. This result enables us to estimate the inter-modular connectivity in various complex systems from the inverse phase synchronization index of the mesoscopic modular activities.

Griffiths phases and localization in hierarchical modular networks.

We study variants of hierarchical modular network models suggested by Kaiser and Hilgetag [ Front. in Neuroinform., 4 (2010) 8] to model functional brain connectivity, using extensive simulations and quenched mean-field theory (QMF), focusing on structures with a connection probability that decays exponentially with the level index. Such networks can be embedded in two-dimensional Euclidean space. We explore the dynamic behavior of the contact process (CP) and threshold models on networks of this kind, including hierarchical trees. While in the small-world networks originally proposed to model brain connectivity, the topological heterogeneities are not strong enough to induce deviations from mean-field behavior, we show that a Griffiths phase can emerge under reduced connection probabilities, approaching the percolation threshold. In this case the topological dimension of the networks is finite, and extended regions of bursty, power-law dynamics are observed. Localization in the steady state is also shown via QMF. We investigate the effects of link asymmetry and coupling disorder, and show that localization can occur even in small-world networks with high connectivity in case of link disorder.

An effective immunization strategy for airborne epidemics in modular and hierarchical social contact network

Social contact between individuals is the chief factor for airborne epidemic transmission among the crowd. Social contact networks, which describe the contact relationships among individuals, always exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated. We find that traditional global targeted immunization strategy would lose its superiority in controlling the epidemic propagation in the social contact networks with modular and hierarchical structure. Therefore, we propose a hierarchical targeted immunization strategy to settle this problem. In this novel strategy, importance of the hierarchical structure is considered. Transmission control experiments of influenza H1N1 are carried out based on a modular and hierarchical network model. Results obtained indicate that hierarchical structure of the network is more critical than the degrees of the immunized targets and the modular network layer is the most important for the epidemic propagation control. Finally, the efficacy and stability of this novel immunization strategy have been validated as well.

A Hierarchical Bayesian Network-Based Approach to Keyword Auction

Prosperity of the online keyword auctions greatly facilitates the penetration of search-engine marketing in various industries. However, the current operation rules of the search engine make it very difficult for those inexperienced advertisers to make sound bids without the support of powerful tools. Therefore, many studies have been conducted to help advertisers understand such dynamic, infinite, and opaque auction situation, and obtain as good as possible bidding result. This paper, focusing on predicting the return on investment (ROI) of a keyword portfolio, develops a hierarchical Bayesian network (BN) model to forecast keyword auctions' performance. Few papers directly predict the ROI of a keyword portfolio. This approach effectively echoes advertisers' expectation for a keyword auction by choosing the right keywords and bids to achieve the desired outcome. The building blocks of the prediction model, such as bid and rank, are organized in a tree-shaped structure with a set of joint conditional probabilities. To infer the posterior probabilities of the predictors, a Bayesian parameter-learning algorithm is conducted after validating the network's structural relationships. The empirical study demonstrates that the prediction model is appropriate and effective for keyword auctions. Moreover, the proposed hierarchical BN model shows a higher accuracy than the popular prediction approach-the back-propagation artificial neural network.

Hierarchical polygonization for generating and updating lane-based road network information for navigation from road markings

Lane-based road network information, such as lane geometry, destination, lane changing, and turning information, is important in vehicle navigation, driving assistance system, and autonomous driving. Such information, when available, is mainly input manually. However, manual methods for creating and updating data are not only costly but also time-consuming, labor-intensive, and prone to long delays. This paper proposes a hierarchical polygonization method for automatic generation and updating of lane-level road network data for navigation from a road marking database that is managed by government transport department created by digitizing or extraction from aerial images. The proposed method extends the hierarchy of a road structure from ‘road–carriageway–lane’ to ‘road–carriageway–lane–basic lane’. Basic lane polygons are constructed from longitudinal road markings, and their associated navigational attributes, such as turning information and speed limit, are obtained from transverse road markings by a feature-in-polygon overlay approach. A hierarchical road network model and detailed algorithms are also illustrated in this paper. The proposed method can accelerate the process of generating and updating lane-level navigation information and can be an important component of a road marking information system for road management.

Database Performance Optimization for SQL Server Based on Hierarchical Queuing Network Model

With the ever-increasing complexity and variety of database workload, database application system has been imposed on higher and higher performance requirements. Database system consists of software and hardware. And the factors that affect database performance are uncertain. In order to tackle the issue of database system for SQL server, this paper proposes hierarchical queuing network model for performance prediction, and a model is established for both software resources and hardware resources, the nested resources are linearized by hierarchical calling, thus finding out the main factors for system performance bottleneck, and system performance is adjusted and optimized accordingly. The performance tuning algorithm for SQL server database based on the hierarchical queuing network is presented in detail. And TPC-C benchmark is adopted for simulation. Experimental results show the proposed method achieves 16.8% performance increase on average, and TPS is improved by 40% compared to previous method.

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT.

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

Hybrid graphical model for semantic image segmentation

To make full use of both non-causal and causal cues in natural images, we propose a hybrid hierarchical Conditional Random Field (HCRF) and Bayesian Network (BN) model for semantic image segmentation in this paper. The HCRF is used to capture non-causal relationship, such as appearance features and inter-class co-occurrence statistics, to produce initial semantic sub-scene predictions. Whereas, the BN is used to model contextual interactions for each semantic sub-scene in the form of class statistics from its neighboring regions, of which its conditional probabilities are learned automatically from training data. The learned BN structure is then used to encode the structure of contextual dependencies for sub-scenes in the initial predictions to generate final refined predictions. Experiments on the Stanford 8-class dataset and the LHI 15-class dataset show that the hybrid model outperforms pure CRF models by 2–4% in average classification accuracy.

Agent Based Modeling on Organizational Dynamics of Terrorist Network

Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.

Crystal networks in silk fibrous materials: from hierarchical structure to ultra performance.

This review provides a comprehensive survey of the structural characteristics of crystal networks of silk soft fibrous materials in correlation with the macroscopic properties/performance and the network formation mechanisms. The correlation between the hierarchical mesoscopic structures and the mechanical properties of silk soft fibrous materials including silk fibroin hydrogels and naturally spun silk fibers are addressed based on the hierarchical crystal network models. Namely, two types of hierarchical networks are identified: the weak nanofibril-nanofibril interaction case (i.e., silk fibroin hydrogels), and the strong nanofibril-nanofibril interaction case (i.e., silk fibers). The macroscopic properties, i.e., the rheological/mechanical properties, can be controlled in terms of tuning different levels of hierarchical network structures by ultrasonication-induced gelation, introducing the initial nucleation centers, etc. Such controls take effect by different mesoscale assembly pathways, which are found to occur via different routes of the nucleation and growth processes. Furthermore, the hierarchical network model of soft fibrous materials can be applied to explain the superior mechanical properties and the unique strain-hardening behaviors of spider silk fibers within the framework of hierarchical breaking mechanism. Obviously, a knowledge of crystal networks will allow the prediction of the performance and engineering strategy of silk fibrous materials in generals.

Topology and simulations of a Hierarchical Markovian Radial Basis Function Neural Network classifier

This paper presents a Hierarchical Markovian Radial Basis Function Neural Network (HiMarkovRBFNN) model that enables recursive operations. The hierarchical structure of this network is composed of recursively nested RBF Neural Networks with arbitrary levels of hierarchy. All hidden neurons in the hierarchy levels are composed of truly RBF Neural Networks with two weight matrices, for the RBF centers and the linear output weights, in contrast to the simple summation neurons with only linear weighted combinations which are usually encountered in ensemble models and cascading networks. Thus the neural network operation in every node is exactly the same at all levels of the hierarchical integration. The hidden RBF response units are recursive. The training methods also remain the same for all levels as in a typical single RBF Neural Network. The simplicity in the neural network construction process is demonstrated by means of three textbook algorithms, namely the well known k-means clustering, the classical tree-based recursion function and the standard regularized least squares solver. Determining centers can be performed top-down and calculation of linear output weights is performed bottom-up. The framework is rather general and optimization algorithms can also be applied. Experimental simulations on various benchmark datasets show that the recursive operation for the hidden RBF response units is promising. Comparisons with the two standard model meta-learning architectures, namely Committee Machines and Cascaded Machines, reveal that the proposed method produces similar results and compares well as a combiner that can merge many HiMarkovRBFNN child nodes into one higher level parent HiMarkovRBFNN node of the same functionality.

Computational modeling of the development of detailed facial representations along ventral pathway

Experimental studies have shown that neurons at an intermediate stage of the primate ventral visual pathway encode the conformation and spatial relations of facial features [1], while neurons in the later stages are selective to the full face [2]. In this study, we investigate how these cell firing properties may develop through visually-guided learning. A hierarchical neural network model of the primate’s ventral visual pathway is trained by presenting many randomly generated faces to the hierarchical competitive neural network while a local learning rule modifies the strengths of the synaptic connections between successive layers [3] (Figure ​(Figure1A).1A). After training, the model is found to have developed the experimentally observed cell firing properties. In particular, we have demonstrated how the primate brain learns to represent facial expression independently of facial identity as reported in [4] (Figure ​(Figure1B).1B). We have also shown how the visual system forms separate representations of facial features such as the eyes, nose and mouth (Figure ​(Figure1C)1C) as well as representations of spatial relationships between these facial features, as have been reported in single unit recording studies [1]. Therefore, this research makes an important contribution to understanding visual processing in the primate brain. Figure 1 A. Stylized image of the 4 layer network model. B. Gabor filter inputs which had strong connectivity through the layers to full face selective neuron (left) Firing activity plots of cells, of which each encodes specific facial identity or expression, ...