Showing papers on "Weighted network published in 2010"

The evolution of the world trade web: a weighted-network analysis

Abstract: This paper employs a weighted network approach to study the empirical properties of the web of trade relationships among world countries, and its evolution over time. We show that most countries are characterized by weak trade links; yet, there exists a group of countries featuring a large number of strong relationships, thus hinting to a core-periphery structure. Also, better-connected countries tend to trade with poorly-connected ones, but are also involved in highly-interconnected trade clusters. Furthermore, rich countries display more intense trade links and are more clustered. Finally, all network properties are remarkably stable across the years and do not depend on the weighting procedure.

Inferring relevant social networks from interpersonal communication

Abstract: Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected to j) from observed communication events (e.g. i emails j). Often overlooked, however, is the impact of tie definition on the corresponding network, and in turn the relevance of the inferred network to the research question of interest. Here we study the problem of network inference and relevance for two email data sets of different size and origin. In each case, we generate a family of networks parameterized by a threshold condition on the frequency of emails exchanged between pairs of individuals. After demonstrating that different choices of the threshold correspond to dramatically different network structures, we then formulate the relevance of these networks in terms of a series of prediction tasks that depend on various network features. In general, we find: a) that prediction accuracy is maximized over a non-trivial range of thresholds corresponding to 5-10 reciprocated emails per year; b) that for any prediction task, choosing the optimal value of the threshold yields a sizable (~30%) boost in accuracy over naive choices; and c) that the optimal threshold value appears to be (somewhat surprisingly) consistent across data sets and prediction tasks. We emphasize the practical utility in defining ties via their relevance to the prediction task(s) at hand and discuss implications of our empirical results.

Weighted complex network analysis of travel routes on the Singapore public transportation system

Abstract: a b s t r a c t The structure and properties of public transportation networks have great implications for urban planning, public policies and infectious disease control. We contribute a complex weighted network analysis of travel routes on the Singapore rail and bus transportation systems. We study the two networks using both topological and dynamical analyses. Our results provide additional evidence that a dynamical study adds to the information gained by traditional topological analysis, providing a richer view of complex weighted networks. For example, while initial topological measures showed that the rail network is almost fully connected, dynamical measures highlighted hub nodes that experience disproportionately large traffic. The dynamical assortativity of the bus networks also differed from its topological counterpart. In addition, inspection of the weighted eigenvector centralities highlighted a significant difference in traffic flows for both networks during weekdays and weekends, suggesting the importance of adding a temporal perspective missing from many previous studies.

International trade and financial integration: a weighted network analysis

Abstract: The authors analyse patterns of international trade and financial integration using complex network analysis. The combination of both binary and weighted approaches delivers more precise and thorough insights into the topological structure and properties of international trade and financial networks (ITN and IFN). It is found that the ITN is more densely connected than the IFN, while both types of network display a core–periphery structure. This hierarchical organization is more pronounced in financial markets, suggesting that the bulk of trade in financial assets occurs through a handful of countries acting as hubs. High-income countries are better linked and form groups of tightly interconnected nodes. This kind of structure can explain why the recent financial crisis has spread rapidly among advanced countries while reaching emerging markets only in a second phase.

DEA and ranking with the network-based approach: a case of R&D performance

Abstract: This study enhances the network-based approach, which is a novel method to increase discrimination in data envelopment analysis. The enhancements include removing the bias caused by a scale difference among organizations and highlighting the approach's ability to identify the strengths and weaknesses of each organization. The former makes the approach applicable to both the constant returns of scale (CRS) and the variable returns of scale (VRS) models. The network-based approach applies the centrality concept developed in social network analysis to discriminate efficient decision making organizations as determined by standard data envelopment analysis (DEA). More specifically, the results of data envelopment analysis are transformed into a directed and weighted network in which each node represents a decision making organization and the link between a pair of node represents the referencing relationship between the pair. The centrality value for each efficient organization provides the base for discrimination and ranking. This network-based approach suggests aggregating DEA results of different input/output combinations such that the merits of each organization under various situations can be considered. The final ranking of this approach favors organizations that have their strengths evenly spread and tends to screen out specialized efficient organizations. As a real world example, the approach is applied to evaluate and rank the R&D (research and development) performance of Taiwan's government-supported research institutes. The cross-organizations and within-organization strengths for each efficient research institute are identified after applying the approach. A two-stage R&D evaluation model separates the R&D process into the technology development and technology diffusion stage. The resulting performance map differentiates the research institutes into four categories—Achievers, Marketers, Innovators, and Underdogs.

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.

Abstract: Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results: In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semisupervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets. Conclusions: We developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: http://home.ustc.edu.cn/~yzh33108/ GeneticInterPred.htm

Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

Abstract: We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This analytic procedure is not only more exact than its numerical alternatives such as LFM and GCE but also much faster. Critical resolution levels can be identified by searching for intervals in which large changes of the resolution do not lead to growth of communities. We tested our algorithm on benchmark graphs and on a network of 492 papers in information science. Combined with a specific post-processing, the algorithm gives much more precise results on LFR benchmarks with high overlap compared to other algorithms and performs very similar to GCE.

Essential proteins discovery from weighted protein interaction networks

Abstract: Identifying essential proteins is important for understanding the minimal requirements for cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them treat all interactions equally and are sensitive to false positives. In this paper, six standard centrality measures are redefined to be used in weighted network. A new method for weighing protein-protein interactions is proposed based on the combination of logistic regression-based model and function similarity. The experimental results on yeast network show that the weighting method can improve the performance of centrality measures considerably. More essential proteins are discovered by the weighted centrality measures than by the original centrality measures used in unweighted network. Even about 20% improvements are obtained from closeness centrality and subgraph centrality.

Effect of tumor resection on the characteristics of functional brain networks

Abstract: Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted network analysis to understand the effect of neurosurgery on the characteristics of functional brain networks. Statistically significant changes in network features have been discovered in the beta (13–30 Hz) band after neurosurgery: the link weight correlation around nodes and within triangles increases which implies improvement in local efficiency of information transfer and robustness; the clustering of high link weights in a subgraph becomes stronger, which enhances the global transport capability; and the decrease in the synchronization or virus spreading threshold, revealed by the increase in the largest eigenvalue of the adjacency matrix, which suggests again the improvement of information dissemination.

Finding communities in directed networks by PageRank random walk induced network embedding

Abstract: Community structure has been found to exist ubiquitously in many different kinds of real world complex networks. Most of the previous literature ignores edge directions and applies methods designed for community finding in undirected networks to find communities. Here, we address the problem of finding communities in directed networks. Our proposed method uses PageRank random walk induced network embedding to transform a directed network into an undirected one, where the information on edge directions is effectively incorporated into the edge weights. Starting from this new undirected weighted network, previously developed methods for undirected network community finding can be used without any modification. Moreover, our method improves on recent work in terms of community definition and meaning. We provide two simulated examples, a real social network and different sets of power law benchmark networks, to illustrate how our method can correctly detect communities in directed networks.

Social network node clustering system and method

Abstract: Users in a social network are represented by nodes on a network graph. A similarity processor generates a similarity matrix of nodes and neighbors. A clustering processor groups select nodes based on similarity. Nodes initially assigned to one cluster are selectively added to other clusters based on similarity. A social network processor provides features and processing based on the clusters of nodes thus produced.

Anonimos: An LP based Approach for Anonymizing Weighted Social Network Graphs

Abstract: The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present Anonimos, a Linear Programming based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anonimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anonimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. Additionally, we demonstrate the composability of different models generated using Anonimos, a property that allows a single anonymized graph to preserve multiple linear properties.

Methods and apparatus for hierarchical routing in communication networks

Abstract: Methods and apparatus for hierarchical routing in communication networks are disclosed. An example hierarchical routing method for a communication network disclosed herein comprises determining a plurality of constrained weighted paths to connect pairs of border nodes of a cluster in the communication network, each constrained weighted path having a respective bandwidth and a respective weight, a constrained weighted path for a pair of border nodes of the cluster being selected, based on a bandwidth threshold, from a set of possible paths capable of connecting the pair of border nodes, and advertising the plurality of constrained weighted paths determined for the cluster.

Social network establishment method and device, and community discovery method and device

Abstract: The invention discloses a social network establishment method and a device, and a community discovery method and a device, and the social network establishment method comprises the following steps: respectively extracting feature words from all information units, and calculating feature vectors which correspond to all the information units according to the feature words; respectively calculating the similarity between each two information units according to the feature vectors; and establishing a social network according to the calculated similarity between each two information units. The method and the device can more really reflect the links among nodes in the network, and better carry out community division on the weighted network.

Synchronization on effective networks

Abstract: The study of network synchronization has attracted increasing attentionrecently. In this paper, we strictly define a class of networks, namely effective networks, which are synchronizable and orientable networks. We can prove that all the effective networks with the same size have the same spectra, and are of the best synchronizability according to the master stability analysis. However, it is found that the synchronization time for different effective networks can be quite different. Further analysis shows that the key ingredient affecting the synchronization time is the maximal depth of an effective network: the larger depth results in a longer synchronization time. The secondary factor is the number of links. The increasing number of links connecting nodes in the same layer (horizontal links) will lead to longer synchronization time, whereas the increasing number of links connecting nodes in neighboring layers (vertical links) will accelerate the synchronization. Our analysis of the relationship between the structure and synchronization properties of the original and effective networks shows that the purely directed effective network can provide an approximation of the original weighted network with normalized input strength. Our findings provide insights into the roles of depth, horizontal and vertical links in the synchronizing process, and suggest that the spectral analysis is helpful yet insufficient for the comprehensive understanding of network synchronization.

Introduction: Using Network Analysis to Understand Innovation

Abstract: The innovation literature has a long-held tradition of using networks to understand processes of idea generation, opportunity recognition and the diffusion of knowledge. This dates back at least to Schumpeter (1912/1983), who talked about the importance of creating new combinations in the innovation process. However, the most dominant use of the network construct in the innovation research context to date is in its qualitative or metaphorical sense. For example, a study might interview a manager and ask them how important their professional network is for generating new ideas.While this has been a productive line of enquiry, new analytical techniques in graph theory (the quantitative analysis of networks) are only just starting to be applied to innovation research. When used to analyse social relationships, graph theory is generally referred to as network or social network analysis. The roots of this approach date back to the studies by Morello in psychology in the 1930s (Freeman, 2004).As network analysis has moved forward, sophisticated techniques in probabilistic network methods, weighted network and longitudinal network analysis have created further possibilities for understanding the interactions between network structures, agents and innovation across multiple levels of analysis. These techniques have been adopted from the physical sciences, and social network analysis has become complex network analysis (Newman, Barabasi and Watts, 2006). When the technical advances are combined with the recent increases in computing power, it has become much more feasible to use complex network analysis more broadly within the social sciences in general, and in innovation studies in particular.From this research we have begun to understand the importance of network structures and the relationship between agents and these structures in the process of innovation. Initial work in this area has focused on specifying the structure of business networks. For example, there have been several papers identifying networks with a 'small world' structure (short average distance through the network combined with high levels of clustering) (Verspagen and Duysters, 2004). More recent work has started to link structural characteristics of networks to innovation performance (Uzzi and Spiro, 2005; Schilling and Phelps, 2007).This special issue of Innovation: Management, Policy & Practice titled 'New Network Perspectives on the Innovation Process' (ISBN 978-1-921348- 32-7) looks at some of the state-of-the-art research incorporating complex network analysis in the study of the innovation process.The first paper by van der Valk and Gijbers (2010) provides an excellent overview of the use of social network analysis in innovation studies, reviewing all 49 papers using network analysis which have been published in the top 10 innovation journals. They then use social network analysis to identify the key issues that these techniques have been used to study: interpersonal and interorganisational collaboration networks, communication networks and technology and sectoral structures. Citation network analysis is one area of wide application for network analysis techniques. This paper provides a good overview of the use of social network analysis within innovation studies, which provides a useful context for the remaining papers in the special issue.The next paper by Maritz (2010) investigates the interactions between networks and entrepreneurial productivity in universities. He shows that academics with larger networks and with more frequent communication within these networks are both more entrepreneurial and more productive. This is an excellent example of the non-structural network papers. It makes extensive use of network concepts and ideas, and it demonstrates the importance of connections in generating novel ideas.Lee and Su (2010) use techniques that are similar to those of van der Valk and Gijsbers, but in this case their focus is on the research literature on regional innovation systems. …

Performance Evaluation of a Topology Control Algorithm for Wireless Sensor Networks

Abstract: A main design challenge in the area of sensor networks is energy efficiency to prolong the network operable lifetime. Since most of the energy is spent for radio communication, an effective approach for energy conservation is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. Assuming that node position information is unavailable, we present a topology control algorithm, termed OTC, for sensor networks. It uses two-hop neighborhood information to select a subset of nodes to be active among all nodes in the neighborhood. Each node in the network selects its own set of active neighbors from among its one-hop neighbors. This set is determined such that it covers all two-hop neighbors. OTC does not assume the network graph to be a Unit Disk Graph; OTC also works well on general weighted network graphs. OTC is evaluated against two well-known algorithms from the literature, namely, Span and GAF through realistic simulations using TOSSIM. In terms of operational lifetime, load balancing and Spanner property OTC shows promising results. Apart from being symmetric and connected, the resulting graph when employing OTC shows good spanner properties.

Inferring the physical connectivity of complex networks from their functional dynamics

Abstract: Biological networks, such as protein-protein interactions, metabolic, signalling, transcription-regulatory networks and neural synapses, are representations of large-scale dynamic systems. The relationship between the network structure and functions remains one of the central problems in current multidisciplinary research. Significant progress has been made toward understanding the implication of topological features for the network dynamics and functions, especially in biological networks. Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure? We modelled the network system by employing a scale-free network of coupled phase oscillators. Pairwise phase coherence (PPC) was calculated for all the pairs of oscillators to present functional dynamics induced by the system. At the regime of global incoherence, we observed a Significant pairwise synchronization only between two nodes that are physically connected. Right after the onset of global synchronization, disconnected nodes begin to oscillate in a correlated fashion and the PPC of two nodes, either connected or disconnected, depends on their degrees. Based on the observation of PPCs, we built a weighted network of synchronization (WNS), an all-to-all functionally connected network where each link is weighted by the PPC of two oscillators at the ends of the link. In the regime of strong coupling, we observed a Significant similarity in the organization of WNSs induced by systems sharing the same substrate network but different configurations of initial phases and intrinsic frequencies of oscillators. We reconstruct physical network from the WNS by choosing the links whose weights are higher than a given threshold. We observed an optimal reconstruction just before the onset of global synchronization. Finally, we correlated the topology of the background network to the observed change of the functional activities in the system. The results presented in this study indicate a strong relationship between the structure and dynamics of complex network systems. As coupling strength increases, synchronization emerges among hub nodes and recruits small-degree nodes. The results show that the onset of global synchronization in the system hinders the reconstruction of an underlying complex structure. Our analysis helps to clarify how the synchronization is achieved in systems of different network topologies.

Increasing reliability of protein interactome by combining heterogeneous data sources with weighted network topological metrics

Abstract: Over the last decade, the development of high-throughput techniques has resulted in a rapid accumulation of protein-protein interaction (PPI) data. However, the high-throughput experimental interaction data is prone to exhibit high level of false-positive and false-negative rates. It is therefore highly desirable to develop an approach to deal with these issues from the computational perspective. Meanwhile, as a variety of genomic and proteomic datasets become available, they provide an opportunity to study the interactions between proteins indirectly. In this paper, we introduce a novel approach that employs the Logistic Regression to integrate heterogeneous types of high-throughput biological data into a weighted biological network. Then, a weighted topological metrics of the network is devised to indicate the interacting possibility of two proteins. We evaluate our method on the Gavin's yeast interaction dataset. The experimental results show that by incorporating heterogeneous data types with weighted network topological metrics, our method improved functional homogeneity and localization coherence compared with existing approaches.

Detecting Communities Using Social Ties

Abstract: Many internet-based applications such as social networking websites, online viral marketing, and recommendation network based applications, use social network analysis to improve performance in terms of user-specific information dissemination. The notion of community in a social network is a key concept in such analyses and there has been significant work recently in identifying communities within a social network. In this paper, we formally define the notion of strength of a link, which was informally introduced by Granovetter, and present a divisive hierarchical clustering method to divide the nodes of a social network into disjoint communities. We also introduce the notion of clustering coefficient as a measure of the quality of a community or cluster. Our experimental results using some well-known benchmark social networks show that our method determines communities with better clustering coefficient than the well known Girvan-Newman method.

Analysis on the weighted knowledge network in industrial cluster

Abstract: There has been a hot stream of research on competitive advantage of enterprises in industrial cluster where members connect with each other by the social behavior of knowledge transfer. The investigation of the relationship among these members can explain the formation of competitive advantage of industrial cluster effectively. The operation of cluster knowledge network is good to form interactive learning mechanism, promote knowledge innovation, and create new advantages. How to promote the formation of innovation networks of industrial clusters becomes an urgent problem of decision-makers to explore and solve gradually. Based on the three-tier model of the enterprise knowledge networks, this study constructs a three-tier network model of the cluster knowledge network to investigate the industry clusters from the network perspective. After reviewing the development of weighted network models, a weighted knowledge network was built using the characteristic variables, such as frequency of communication and clustering of communication. A dynamical model measuring knowledge flow was set up to characterize the knowledge intensity of the cluster members, which in term, will also provide a reference for future study on weighted knowledge network.

Identifying key players in a social network: Measuring the extent of an individual's Neighbourhood Connectivity

Abstract: In this paper, we argue that only sufficient increase in distance between the nodes which can virtually disconnect the graph is necessary instead of the physical disconnection of the network to identify the key players in a network A procedure is described for finding sets of key players in a social network A key assumption is that the importance of a node v not only depends on the cohesion of its personal network but also depends on, to which extent the immediate neighbors and indirect contacts of node v are connected with each other Therefore, a new measure of importance of a node in a network has been described which suggests the best node whose removal maximally increases the distances between rests of the nodes to virtually disconnect the graph at-least Moreover, an algorithm based on network disruption has also been described which uses the proposed measure and chooses a single or group of nodes as key players in a network

RESEARCH ARTICLE Grooming Network Cohesion and the Role of Individuals in a Captive Chimpanzee Group

Abstract: Social network analysis offers new tools to study the social structure of primate groups. We used social network analysis to investigate the cohesiveness of a grooming network in a captive chimpanzee group (N 5 17) and the role that individuals may play in it. Using data from a year-long observation, we constructed an unweighted social network of preferred grooming interactions by retaining only those dyads that groomed above the group mean. This choice of criterion was validated by the finding that the properties of the unweighted network correlated with the properties of a weighted network (i.e. a network representing the frequency of grooming interactions) constructed from the same data. To investigate group cohesion, we tested the resilience of the unweighted grooming network to the removal of central individuals (i.e. individuals with high betweenness centrality). The network fragmented more after the removal of individuals with high betweenness centrality than after the removal of random individuals. Central individuals played a pivotal role in maintaining the network’s cohesiveness, and we suggest that this may be a typical property of affiliative networks like grooming networks. We found that the grooming network correlated with kinship and age, and that individuals with higher social status occupied more central positions in the network. Overall, the grooming network showed a heterogeneous structure, yet did not exhibit scale-free properties similar to many other primate networks. We discuss our results in light of recent findings on animal social networks and chimpanzee grooming. Am. J. Primatol. 71:1–10, 2010. r 2010 Wiley-Liss, Inc.