Graph Models For Networks Research Articles

A new algorithm based on bipartite graph networks for improving aggregate recommendation diversity

Most of the traditional recommendation algorithms focus on the accuracy of recommendation results; however, the diversity of recommendation results is also important, which can be used to avoid the long-tail phenomenon. In this paper, a new algorithm for improving aggregate recommendation diversity is proposed. Firstly, a candidate recommendation list based on predictive scores is constructed; and then a bipartite graph network model is constructed. Secondly, item capacity is set to limit the number of recommendations of popular items. Finally, the final recommendation result is generated by combining the recommendation augmenting path. Based on the real world movie rating datasets, experiment results show that the proposed algorithm can effectively guarantee the accuracy of the recommendation results as well as improved the aggregate diversity of the recommendation.

Menopause and big data: Word Adjacency Graph modeling of menopause-related ChaCha data.

To detect and visualize salient queries about menopause using Big Data from ChaCha. We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content. We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263,363 queries from individuals aged 13 and older and the second contained 5,892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment. We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.

A time-dependent model with speed windows for share-a-ride problems: A case study for Tokyo transportation

This paper introduces a new fully time-dependent model of a public transportation system in the urban context that allows sharing a taxi between one passenger and parcels with speed widows consideration. The model contains many real-life case features and is presented by a mathematical formulation. We study both static and dynamic scenarios in comparison to traditional strategies, i.e., the direct delivery model. Moreover, we classify speed windows by different zones and congestion levels during a day in the urban context. Different speed windows induce the dynamic graph model for road networks and make the problem much more difficult to solve. Because of the complex model, the preprocessing steps on data as well as on dynamic graphs are very important. We use a greedy algorithm to initiate the solution and then use some local search techniques to improve the solution quality. The experimental data set is recorded by Tokyo-Musen Taxi company. The data set includes more than 20000 requests per day, more than 4500 used taxis per day and more than 130000 crossing points on the Tokyo map. Experimental results are analyzed on various factors such as the total benefit, the accumulating traveling time during the day, the number of used taxis and the number of shared requests.

Functional models and extending strategies for ecological networks

Complex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management.

Information seeking in secondary schools: A multilevel network approach

In this study, we investigate information seeking interactions in secondary schools from a multilevel network approach. Based on network-related theories, we examine the facilitating role of formal subunits. We apply exponential random graph models for multilevel networks and summarize our findings by using a meta-analysis technique. Our results indicate that formal subunits (e.g. subject departments) can, to some extent, facilitate interactions, in loosely coupled organizations (e.g. secondary schools). Finally, this study shows that a multilevel network approach can provide a more informative representation of information seeking ties in knowledge-intensive organizations.

A study on decision-making of food supply chain based on big data

As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value from big data.

The wide-diameter of [formula omitted

The l-wide-diameter of a graph G is the minimum integer d for which there exist at least l internally disjoint paths of length at most d between any two distinct vertices in G. This parameter measures the fault tolerance and transmission delay in communication networks modelled by graphs. Hypercube-like graphs are widely used as graph models for interconnection networks. In this paper, we study the wide-diameters of a special type of hypercube-like graphs: the Z-cubes Zn,k. It is known that Zn,k have diameter at most ⌈nk+1⌉+2k in Zhu (2015). We show that the k-wide-diameter of Zn,k is at most ⌈nk+1⌉+2k+k+4. In particular, if k=⌈log2n−2log2log2n⌉, then the k-wide-diameter of Zn,k is (1+o(1))nlog2n, which is asymptotically optimal.

ПРОГРАММНАЯ РЕАЛИЗАЦИЯ, АНАЛИЗ ЭФФЕКТИВНОСТИ И ОЦЕНКА КАЧЕСТВА АЛГОРИТМОВ КЛАСТЕРИЗАЦИИ ГРАФОВЫХ МОДЕЛЕЙ СОЦИАЛЬНЫХ СЕТЕЙ

ПРОГРАММНАЯ РЕАЛИЗАЦИЯ, АНАЛИЗ ЭФФЕКТИВНОСТИ И ОЦЕНКА КАЧЕСТВА АЛГОРИТМОВ КЛАСТЕРИЗАЦИИ ГРАФОВЫХ МОДЕЛЕЙ СОЦИАЛЬНЫХ СЕТЕЙ

Rumors Spread Slowly in a Small-World Spatial Network

Rumor spreading is a protocol for modeling the spread of information through a network via user-to-user interaction. The spatial preferred attachment (SPA) model is a random graph model for complex networks: Vertices are placed in a metric space, and the link probability depends on the metric distance between vertices and on their degree. We show that the SPA model typically produces graphs that have small effective diameter, i.e., $O(\log^2 n)$, while rumor spreading is relatively slow, namely, polynomial in $n$.

The clustering methods in graph models of social networks

The clustering methods in graph models of social networks

A Big Data Decision-making Mechanism for Food Supply Chain

Many companies have captured and analyzed huge volumes of data to improve the decision mechanism of supply chain, this paper presents a big data harvest model that uses big data as inputs to make more informed decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates foods demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the decision-making mechanism has vast potential by extracting value from big data.

Forging a link between mentoring and collaboration: a new training model for implementation science.

BackgroundTraining investigators for the rapidly developing field of implementation science requires both mentoring and scientific collaboration. Using social network descriptive analyses, visualization, and modeling, this paper presents results of an evaluation of the mentoring and collaborations fostered over time through the National Institute of Mental Health (NIMH) supported by Implementation Research Institute (IRI).MethodsData were comprised of IRI participant self-reported collaborations and mentoring relationships, measured in three annual surveys from 2012 to 2014. Network descriptive statistics, visualizations, and network statistical modeling were conducted to examine patterns of mentoring and collaboration among IRI participants and to model the relationship between mentoring and subsequent collaboration.ResultsFindings suggest that IRI is successful in forming mentoring relationships among its participants, and that these mentoring relationships are related to future scientific collaborations. Exponential random graph network models demonstrated that mentoring received in 2012 was positively and significantly related to the likelihood of having a scientific collaboration 2 years later in 2014 (p = 0.001). More specifically, mentoring was significantly related to future collaborations focusing on new research (p = 0.009), grant submissions (p = 0.003), and publications (p = 0.017). Predictions based on the network model suggest that for every additional mentoring relationships established in 2012, the likelihood of a scientific collaboration 2 years later is increased by almost 7 %.ConclusionsThese results support the importance of mentoring in implementation science specifically and team science more generally. Mentoring relationships were established quickly and early by the IRI core faculty. IRI fellows reported increasing scientific collaboration of all types over time, including starting new research, submitting new grants, presenting research results, and publishing peer-reviewed papers. Statistical network models demonstrated that mentoring was strongly and significantly related to subsequent scientific collaboration, which supported a core design principle of the IRI. Future work should establish the link between mentoring and scientific productivity. These results may be of interest to team science, as they suggest the importance of mentoring for future team collaborations, as well as illustrate the utility of network analysis for studying team characteristics and activities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-016-0499-y) contains supplementary material, which is available to authorized users.

A two-hop neighbor preference-based random network graph model with high clustering coefficient for modeling real-world complex networks

The Erdos-Renyi (ER) random network model generates graphs under the assumption that there could exist a link u-v between two nodes u and v irrespective of whether or not the two nodes had a common neighbor before the establishment of the link. As a result, random network graphs generated under the ER model are characteristic of having a low clustering coefficient (a measure of the probability for a link to exist between any two neighbors of a node) and low variation in the node degrees, and hence could not match closely to graphs abstracting real-world networks. In this paper, we propose a random network graph model that gives preference to closing the triangle involving three nodes u, w and v with existing links u-w and w-v (i.e., node v is strictly a two-hop neighbor of node u). Accordingly, when node u is looking for a new link to be setup with some other node x, we consider x along with the two-hop neighbors of u and choose one among these nodes with a probability plink as the new neighbor of node u. The proposed Two-Hop Neighbor Preference (THNP)-based model generates random graphs whose clustering coefficient decreases with increase in node degree: matching closely to several real-world network graphs that are commonly studied for complex network analysis.

Evaluation of noise removal algorithms for imaging and reconstruction of vascular networks using micro-CT

Micro-computed tomography systems are widely used for high-resolution, non-destructive analysis of internal microvascular networks. When the scale of the targeted vessel approaches the imaging resolution limit, the level of noise becomes a limiting factor for accurate reconstruction. Denoising algorithms provided by vendors are often suboptimal for enhancing SNR of fine (vessel) features. Furthermore, the performance of existing methods has not been systematically analyzed in the context of final network reconstruction and graph model extraction. This work evaluates several standard and state-of-the-art noise reduction techniques using both in silico and physical phantoms, and ex vivo rat coronary data for their ability to improve vascular network analysis. We compared five noise reduction approaches, including vendor-supplied (Gaussian smoothing), conventional (median filter) and advanced (i.e. wavelet filter with soft thresholding, block-matching collaborative filtering (BM3D), and isotropic and anisotropic total variation denoising) techniques. The latter two methods were chosen for their reported ability to preserve fine details, a prerequisite for a successful microvascular extraction. The full evaluation pipeline included the reconstruction from projection images, denoising, vascular segmentation and graph model extraction to be performed on all simulated and real image data sets. SNR, CNR and 3D NPS were quantified from denoised images, and where the ground truth was known, Sørensen–Dice coefficients, Jaccard index metrics were calculated as measures of segmentation error. The performance of the image denoising algorithms where the ground-truth was available has been assessed by computing the correlation coefficients between the residual images (obtained between the noise-free data and the denoised data) and the first derivative of the noise-free data were computed. Overall, simpler denoising techniques including the median and wavelet filters and the vendor-supplied implementations have been found to perform inadequately for segmentation of fine vessel features, particularly on real images. BM3D technique performed well in most of our tests, however isotropic total variation (ITV) was the optimal choice for noise reduction and feature preservation in real data as shown by the extracted network models. Globally, ITV increased the SNR from 10.2 to 31.7 dB in a Shepp Logan phantom, doubled SNR and CNR values in a scanned physical phantom compared with BM3D, enabled the smallest vessels to be fully recovered in an in silicon phantom and achieved a near-ideal outcome in the rat coronary data.

Fast image quality assessment via supervised iterative quantization method

No-reference/Blind image quality assessment (NR-IQA/BIQA) is significant for image processing and yet very challenging, especially for real-time application and big image data processing. Traditional NR-IQA metrics usually train complex models such as support vector machine, neural network, and probability graph model, which result in long computational time and poor robustness. To overcome these weaknesses, the paper proposes a fast no-reference image quality assessment via hash coding method, named NRHC. First, the image is divided into overlapped patches to extract the spatial statistical features of natural scene images. Then the features are encoded to produce binary hash codes via supervised iterative quantization (SITQ) method. Finally, the Hamming distances between the hash code of the test image and those of original undistorted images are calculated to obtain the final image quality. Thorough experiments on benchmark databases demonstrate that the proposed approach achieves comparable performance and has higher computational efficiency and stronger robustness compared with the state-of-the-art NR-IQA methods.

A geometric graph model for citation networks of exponentially growing scientific papers

In citation networks, the content relativity of papers is a precondition of engendering citations, which is hard to model by a topological graph. A geometric graph is proposed to predict some features of the citation networks with exponentially growing papers, which addresses the precondition by using coordinates of nodes to model the research contents of papers, and geometric distances between nodes to diversities of research contents between papers. Citations between modeled papers are drawn according to a geometric rule, which addresses the precondition as well as some other factors engendering citations, namely academic influences of papers, aging of those influences, and incomplete copying of references. Instead of cumulative advantage of degree, the model illustrates that the scale-free property of modeled networks arises from the inhomogeneous academic influences of modeled papers. The model can also reproduce some other statistical features of citation networks, e.g. in- and out-assortativities, which show the model provides a suitable tool to understand some aspects of citation networks by geometry.

Snowball sampling for estimating exponential random graph models for large networks

The exponential random graph model (ERGM) is a well-established statistical approach to modelling social network data. However, Monte Carlo estimation of ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of full networks that can be fitted. We demonstrate the use of snowball sampling and conditional estimation to estimate ERGM parameters for large networks, with the specific goal of studying the validity of inference about the presence of such effects as network closure and attribute homophily. We estimate parameters for snowball samples from the network in parallel, and combine the estimates with a meta-analysis procedure. We assess the accuracy of this method by applying it to simulated networks with known parameters, and also demonstrate its application to networks that are too large (over 40000 nodes) to estimate social circuit and other more advanced ERGM specifications directly. We conclude that this approach offers reliable inference for closure and homophily.

A geometric graph model for coauthorship networks

Modeling coauthorship networks helps to understand the emergence and propagation of thoughts in academic society. A random geometric graph is proposed to model coauthorship networks, the connection mechanism of which expresses the effects of the academic influences and homophily of authors, and the collaborations between research teams. Our analysis reveals that the modeled networks have a range of features of empirical coauthorship networks, namely, the degree distribution made up of a mixture Poisson distribution with a power-law tail, clear community structure, small-world, high clustering, and degree assortativity. Moreover, the underlying formulae of the tail and forepart of the degree distribution, and the tail of the scaling relation between local clustering coefficient and degree are derived for the modeled networks, and are also applicable to the empirical networks.

Graph Models of Network Behavior in Environmental Planning

Policies to protect the environment in Europe and in the rest of the world have been adjusted to take into account the network behavior of conglomerates of nature protection areas. Network behavior can emerge from the natural configuration of habitat patches, or be induced by the establishment of habitat corridors. Careful planning is required to protect and improve the network behavior in existing sites; this has prompted researchers to build graph models of ecological networks, and apply complex network analysis to improve the understanding of their features. However, the most common approach is to keep the focus on a single species, meant to be representative of most species within the area under analysis, or especially important with respect to conservation issues. In this paper, data pertaining to land use types found within sites making up the “Natura 2000” ecological network is used to provide a high-level view of the network, and propose a framework for study, in which similarity measures are used as a criterion to suggest guidelines for land management.

Delay-Optimal Data Forwarding in Vehicular Sensor Networks

Vehicular Sensor Network (VSN) is emerging as a new solution for monitoring urban environments such as Intelligent Transportation Systems and air pollution. One of the crucial factors that determine the service quality of urban monitoring applications is the delivery delay of sensing data packets in the VSN. In this paper, we study the problem of routing data packets with minimum delay in the VSN, by exploiting i) vehicle traffic statistics, ii) anycast routing and iii) knowledge of future trajectories of vehicles such as buses. We first introduce a novel road network graph model that incorporates the three factors into the routing metric. We then characterize the packet delay on each edge as a function of the vehicle density, speed and the length of the edge. Based on the network model and delay function, we formulate the packet routing problem as a Markov Decision Process (MDP) and develop an optimal routing policy by solving the MDP. Evaluations using real vehicle traces in a city show that our routing policy significantly improves the delay performance compared to existing routing protocols.