Showing papers on "Weighted network published in 2012"

Control Centrality and Hierarchical Structure in Complex Networks

Abstract: We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.

Exponential-family random graph models for valued networks.

Abstract: Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.

Generalized master equations for non-Poisson dynamics on networks.

Abstract: The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed by Poisson processes, which is not typically the case in empirical temporal networks. Accordingly, we examine the effects of non-Poisson inter-event statistics on the dynamics of edges, and we apply the concept of a generalized master equation to the study of continuous-time random walks on networks. We show that this equation reduces to the standard rate equations when the underlying process is Poissonian and that its stationary solution is determined by an effective transition matrix whose leading eigenvector is easy to calculate. We conduct numerical simulations and also derive analytical results for the stationary solution under the assumption that all edges have the same waiting-time distribution. We discuss the implications of our work for dynamical processes on temporal networks and for the construction of network diagnostics that take into account their nontrivial stochastic nature.

Graphlet decomposition of a weighted network

Abstract: We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable algorithm, which combines EM with Bron-Kerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one network sample. We explore theoretical properties of graphlets, including computational complexity, redundancy and expected accuracy. We test graphlets on synthetic data, and we analyze messaging on Facebook and crime associations in the 19th century.

The Length of Bridge Ties: Structural and Geographic Properties of Online Social Interactions

Abstract: The popularity of the Web has allowed individuals to communicate and interact with each other on a global scale: people connect both to close friends and acquaintances, creating ties that can bridge otherwise separated groups of people. Recent evidence suggests that spatial distance is still affecting social links established on online platforms, with online ties preferentially connecting closer people. In this work we study the relationships between interaction strength, spatial distance and structural position of ties between members of a large-scale online social networking platform, Tuenti. We discover that ties in highly connected social groups tend to span shorter distances than connections bridging together otherwise separated portions of the network. We also find that such bridging connections have lower social interaction levels than ties within the inner core of the network and ties connecting to its periphery. Our results suggest that spatial constraints on online social networks are intimately connected to structural network properties, with important consequences for information diffusion.

A cross-layer design for QoS support in cognitive radio sensor networks for smart grid applications

Abstract: In this paper, we propose a cross-layer design to meet the QoS requirements for smart grids employing the cognitive radio sensor networks for their control and monitoring operations. Existing routing protocols pertaining to QoS support are not able to simultaneously handle traffic of different characteristics present in smart grids. Therefore, considering the traffic heterogeneity of smart grid applications exhibiting diverse QoS requirements, a set of priority classes is defined in order to differentiate the traffic for the respective service. Specifically, the problem is formulated as a weighted network utility maximization (WNUM) whose objective is to maximize the weighted sum of flows service. A cross-layer heuristic solution is provided to solve the utility optimization problem by performing joint routing, dynamic spectrum allocation and medium access. Performance of the proposed protocol is evaluated using ns-2, which shows that the number of flows belonging to each class are served according to their weight fraction with their respective data rate, latency and reliability requirement.

Network coordination and synchronization in a noisy environment with time delays.

Abstract: We study the effects of nonzero time delays in stochastic synchronization problems with linear couplings in complex networks. We consider two types of time delays: transmission delays between interacting nodes and local delays at each node (due to processing, cognitive, or execution delays). By investigating the underlying fluctuations for several delay schemes, we obtain the synchronizability threshold (phase boundary) and the scaling behavior of the width of the synchronization landscape, in some cases for arbitrary networks and in others for specific weighted networks. Numerical computations allow the behavior of these networks to be explored when direct analytical results are not available. We comment on the implications of these findings for simple locally or globally weighted network couplings and possible trade-offs present in such systems.

Anónimos: 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. We also demonstrate the composability of different models generated using Anonimos, a property that allows a single anonymized graph to preserve multiple linear properties.

Supervised maximum-likelihood weighting of composite protein networks for complex prediction

Abstract: Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI) data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to discover novel complexes. We show improved performance over previous approaches in terms of precision, recall, and number and quality of novel predictions. We present and visualize two novel predicted complexes in yeast and human, and find external evidence supporting these predictions.

Scale-free structures emerging from co-evolution of a network and the distribution of a diffusive resource on it.

Abstract: Co-evolution exhibited by a network system, involving the intricate interplay between the dynamics of the network itself and the subsystems connected by it, is a key concept for understanding the self-organized, flexible nature of real-world network systems. We propose a simple model of such coevolving network dynamics, in which the diffusion of a resource over a weighted network and the resource-driven evolution of the link weights occur simultaneously. We demonstrate that, under feasible conditions, the network robustly acquires scale-free characteristics in the asymptotic state. Interestingly, in the case that the system includes dissipation, it asymptotically realizes a dynamical phase characterized by an organized scale-free network, in which the ranking of each node with respect to the quantity of the resource possessed thereby changes ceaselessly. Our model offers a unified framework for understanding some real-world diffusion-driven network systems of diverse types.

Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes

Abstract: In this paper, cluster synchronization in community network with nonidentical nodes and impulsive effects is investigated. Community networks with two kinds of topological structure are investigated. Positive weighted network is considered first and external pinning controllers are designed for achieving cluster synchronization. Cooperative and competitive network under some assumptions is investigated as well and can achieve cluster synchronization with only impulsive controllers. Based on the stability analysis of impulsive differential equation and the Lyapunov stability theory, several simple and useful synchronization criteria are derived. Finally, numerical simulations are provided to verify the effectiveness of the derived results.

Identifying Important Nodes in Weighted Covert Networks Using Generalized Centrality Measures

Abstract: For investigators working on criminal covert networks, identification of key actor(s) in the network is a major objective. Taking out key nodes will decrease the ability of the criminal network to function normally. Traditionally, the node centrality measurements have relied solely on the number of edges incident to nodes but not on the weights of those edges. However, in some generalizations for centrality measures for weighted networks, the focus shifts solely to the weights of the links and they don't account for the number of ties which was the central idea in the original centrality measures. Hence, answering which nodes are most central in a network with weighted relations depends on what imporantce is given the weights of the incident edges in comparison to the number of those edges. Opsahl et al. propose a generalized method for controlling the relative importance between the number of incident ties (nodal degree) versus the total weight of those ties (nodal strength). Research in TNA has largely focused on un-weighted ties, whereas richer and more sophisticated models of covert networks are needed to give precise and more realistic knowledge about such networks. Moreover, the existing implementation of node centrality algorithms in TNA tools don't support networks having weighted/values relations among nodes. New implementations of node centrality algorithms for weighted networks based on the generalized approach have been developed in Crime Fighter Assistant tool and are evaluated with known network dataset of the 9/11 incident.

A Numerical Approach to the Merging of Argumentation Networks

Abstract: In this paper, we propose a numerical approach to the problem of merging of argumentation networks. The idea is to consider an augmented network containing the arguments and attacks of all networks to be merged and then associate a weight to each of its components based on how they are perceived by the agents associated with the local networks. The combined weighted network is then used to define a system of equations from which the overall strength of the arguments is calculated.

Discovering Communities through Friendship

Abstract: We introduce a new method for detecting communities of arbitrary size in an undirected weighted network. Our approach is based on tracing the path of closest‐friendship between nodes in the network using the recently proposed Generalized Erds Numbers. This method does not require the choice of any arbitrary parameters or null models, and does not suffer from a system‐size resolution limit. Our closest‐friend community detection is able to accurately reconstruct the true network structure for a large number of real world and artificial benchmarks, and can be adapted to study the multi‐level structure of hierarchical communities as well. We also use the closeness between nodes to develop a degree of robustness for each node, which can assess how robustly that node is assigned to its community. To test the efficacy of these methods, we deploy them on a variety of well known benchmarks, a hierarchal structured artificial benchmark with a known community and robustness structure, as well as real‐world networks of coauthorships between the faculty at a major university and the network of citations of articles published in Physical Review. In all cases, microcommunities, hierarchy of the communities, and variable node robustness are all observed, providing insights into the structure of the network.

Identification of Critical Lines and Nodes in Power Grid Based on Maximum Flow Transmission Contribution Degree

Abstract: A new method for assessing the critical lines and nodes of power grid from the structural perspective is proposed.The transmission contribution degree is defined to identify the critical lines and nodes based on the maximum flow and the complex network theories,and characterizes the ability of lines and nodes to undertake the transmission of power flow in the grid.The method can overcome the limitation of the hypothesis that power flow is transferred along the shortest path compared with the present research,and take all the possible transmission paths between different "source-load" pairs into consideration based on the whole structure of the network.Meanwhile the power gird is considered as a directed and weighted network with the power transfer ability of line taken into account,making the physical background even more suitable for the actual condition of the power system.The test on an IEEE 39-bus system demonstrates the rationality and validity in comparison with the methods available.

Degree centrality based on the weighted network

Abstract: Node centrality has been widely studied in the complex networks. In 2010, the model of node centrality under the weighted network was obtained by Tore Opashl et al. Tie weights and the number of ties were connected with certain proportion by tuning parameter in the model. However, the proportion is random measure. In this paper, the selection standard of the optimal turning parameters is proposed. In the proposed method, the maximum degree centrality of node can be emphasized. The numerical example of weighted network on optimal value selection is used to show the efficiency of the method

Mathematical model for spreading dynamics of social network worms

Abstract: In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks.

Information Communities: The Network Structure of Communication

Abstract: This study puts forward a variable clique overlap model for identifying information communities, or potentially overlapping subgroups of network actors among whom reinforced independent links ensure efficient communication. We posit that the intensity of communication between individuals in information communities is greater than in other areas of the network. Empirical tests show that the variable clique overlap model is more useful for identifying groups of individuals that have strong internal relationships in closed networks than those defined by more general models of network closure. These findings extend the scope of network closure effects proposed by other researchers working with communication networks using social network methods and approaches, a tradition which emphasizes ties between organizations, groups, individuals, and the external environment.

Collecting information by power-aware mobile agents

Abstract: A set of identical, mobile agents is deployed in a weighted network. Each agent possesses a battery - a power source allowing the agent to move along network edges. Agents use their batteries proportionally to the distance traveled. At the beginning, each agent has its initial information. Agents exchange the actually possessed information when they meet. The agents collaborate in order to perform an efficient convergecast , where the initial information of all agents must be eventually transmitted to some agent. The objective of this paper is to investigate what is the minimal value of power, initially available to all agents, so that convergecast may be achieved. We study the question in the centralized and the distributed settings. In the distributed setting every agent has to perform an algorithm being unaware of the network. We give a linear-time centralized algorithm solving the problem for line networks. We give a 2-competitive distributed algorithm achieving convergecast for tree networks. The competitive ratio of 2 is proved to be the best possible for this problem, even if we only consider line networks. We show that already for the case of tree networks the centralized problem is strongly NP-complete. We give a 2-approximation centralized algorithm for general graphs.

Link Prediction Using BenefitRanks in Weighted Networks

Abstract: Link prediction in weighted network is an important task in Social Network Analysis. This problem aims at determining missing links in weighted networks. By taking advantage of the weights and structural information of networks, a mechanism for rating nodes' authorities in terms of the value of weight, called Benefit Rank, is defined. This mechanism can flexibly collect different order neighbors' information of nodes to complete the rating authority process for each node in weighted networks. Using Benefit Rank combined with the Weak Ties theory, similarity measures are proposed to estimate the emergence of future relationships between nodes in weighted networks. Extensive experiments were carried out on four real weighted networks. Compared with existing methods, our methods can provide higher accuracy for link prediction in weighted networks.

Directed Networks of Online Chats: Content-Based Linking and Social Structure

Abstract: Online chats are recently shown to result in long term associations among users, represented by a directed weighted network, similar to dialogs in online social networks. We consider the persistent network which emerges from user-to-user communications found in the empirical dataset from IRC Ubuntu channel. The structure of these networks is determined by computing topological centrality measures, link correlations and community detection, and by testing validity of the "social ties" hypothesis. To unravel underlying linking mechanisms, we further study type of messages exchanged among users and users with Web bots, and their emotional content, annotated in the texts of messages. We find that the ranking of the users according to the frequency of their messages obeys Zipf's law with a unique exponent for each message type. Furthermore, the specific hierarchical structure of the network with a strong core as well as its social organization are shown to be closely related with the most frequently used message types and the amount of emotional arousal in them.

DCR-based causal design knowledge evaluation method and system for future CAD applications

Abstract: This paper presents a new causal design knowledge evaluation method and system for future CAD applications. Current product development processes still include unintended feedback due to insufficient product design knowledge. Previous research on design knowledge support system focuses on search by matching keywords and file names, or search by specific indices, which has various drawbacks. Furthermore, current CAD systems need manual input to incorporate the designer's knowledge. To systematize the knowledge management process for the next-generation CAD systems, a prerequisite is to capture ever-evolving causal design knowledge. In this paper, we present a new causal knowledge network evaluation method, which has not been well addressed in design knowledge support system research. For the network evaluation, we present a degree of causal representation (DCR)-based knowledge network evaluation method. In this method, causality and network connectivity are used for the causal knowledge network with weighted vertices and weighted network connectivity for a network with weighted edges. To validate the proposed method, this evaluation method has been compared with structural measures. Finally, the causal design knowledge evaluation system, KNOES, is implemented and tested with a new valve design scenario.

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Abstract: Social networks existing among employees, customers or users of various IT systems have become one of the research areas of growing importance. A social network consists of nodes - social entities and edges linking pairs of nodes. In regular, one-layered social networks, two nodes - i.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Nowadays data about people and their interactions, which exists in all social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but also gain understanding about semantic of human relations. Are they direct or not? Do people tend to sustain single or multiple relations with a given person? What types of communication is the most important for them? Answers to these and more questions enable us to draw conclusions about semantic of human interactions. Unfortunately, most of the methods used for social network analysis (SNA) may be applied only to one-layered social networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper, in particular: cross-layer clustering coefficient, cross-layer degree centrality and various versions of multi-layered degree centralities. Authors also investigated the dynamics of multi-layered neighbourhood for five different layers within the social network. The evaluation of the presented concepts on the real-world dataset is presented. The measures proposed in the paper may directly be used to various methods for collective classification, in which nodes are assigned to labels according to their structural input features.

Graphlet decomposition of a weighted network

Abstract: We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable inference algorithm, which combines EM with Bron-Kerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one network sample. We explore some theoretical properties of the graphlet decomposition, including computational complexity, redundancy and expected accuracy. We demonstrate graphlets on synthetic and real data. We analyze messaging patterns on Facebook and criminal associations in the 19th century.

Diversity dynamics in online networks

Abstract: Diversity is an important characterization aspect for online social networks that usually denotes the homogeneity of a network's content and structure. This paper addresses the fundamental question of diversity evolution in large-scale online communities over time. In doing so, we study different established notions of network diversity, based on paths in the network, degree distributions, eigenvalues, cycle distributions, and control models. This leads to five appropriate characteristic network statistics that capture corresponding aspects of network diversity: effective diameter, Gini coefficient, fractional network rank, weighted spectral distribution, and number of driver nodes of a network. Consequently, we present and discuss comprehensive experiments with a broad range of directed, undirected, and bipartite networks from several different network categories -- including hyperlink, interaction, and social networks. An important general observation is that network diversity shrinks over time. From the conceptual perspective, our work generalizes previous work on shrinking network diameters, putting it in the context of network diversity. We explain our observations by means of established network models and introduce the novel notion of eigenvalue centrality preferential attachment.

WNP: a novel algorithm for gene products annotation from weighted functional networks.

Abstract: Predicting the biological function of all the genes of an organism is one of the fundamental goals of computational system biology. In the last decade, high-throughput experimental methods for studying the functional interactions between gene products (GPs) have been combined with computational approaches based on Bayesian networks for data integration. The result of these computational approaches is an interaction network with weighted links representing connectivity likelihood between two functionally related GPs. The weighted network generated by these computational approaches can be used to predict annotations for functionally uncharacterized GPs. Here we introduce Weighted Network Predictor (WNP), a novel algorithm for function prediction of biologically uncharacterized GPs. Tests conducted on simulated data show that WNP outperforms other 5 state-of-the-art methods in terms of both specificity and sensitivity and that it is able to better exploit and propagate the functional and topological information of the network. We apply our method to Saccharomyces cerevisiae yeast and Arabidopsis thaliana networks and we predict Gene Ontology function for about 500 and 10000 uncharacterized GPs respectively.

Weighted Network Model Based Recognition of Dangerous Lines Under Power Flow Transferring

Abstract: In allusion to the incompleteness of existing method to search the path by which power flow is transferred,a weighted network model based method to recognize dangerous line under power flow transferring is proposed.A comprehensive analysis on topological structure of power grid and the imbalanced power flow distribution is performed,on this basis two kinds of weighted power grid models,in which the line reactances and the reciprocals of loading rates of transmission lines are taken as the edge weights respectively,are built;then the shortest transmission path of the two models are searched by Floyd algorithm,and the transmission lines included in the search result are the dangerous lines.In the proposed method the influence of heterogeneity of connective nodes on power flow transferring is synthetically considered,thus the lines containing transformers that are directly connected with generators can be effectively recognized and the dangerous lines obtained by the recognition under power flow transferring are more exact.The correctness and effectiveness of the proposed method are verified by simulation analysis based on IEEE 30-bus system.