Showing papers on "Betweenness centrality published in 2018"

A systematic survey of centrality measures for protein-protein interaction networks

Abstract: Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

From the betweenness centrality in street networks to structural invariants in random planar graphs.

Abstract: The betweenness centrality, a path-based global measure of flow, is a static predictor of congestion and load on networks. Here we demonstrate that its statistical distribution is invariant for planar networks, that are used to model many infrastructural and biological systems. Empirical analysis of street networks from 97 cities worldwide, along with simulations of random planar graph models, indicates the observed invariance to be a consequence of a bimodal regime consisting of an underlying tree structure for high betweenness nodes, and a low betweenness regime corresponding to loops providing local path alternatives. Furthermore, the high betweenness nodes display a non-trivial spatial clustering with increasing spatial correlation as a function of the edge-density. Our results suggest that the spatial distribution of betweenness is a more accurate discriminator than its statistics for comparing static congestion patterns and its evolution across cities as demonstrated by analyzing 200 years of street data for Paris.

Enhance the Performance of Network Computation by a Tunable Weighting Strategy

Abstract: Networked systems with high computational efficiency are desired in many applications ranging from sociology to engineering. Generally, the performance of the network computation can be enhanced by two ways: rewiring and weighting. In this paper, we proposed a new two-modes weighting strategy based on the concept of communication neighbor graph , which takes use of both the local and global topological properties, e.g., degree centrality, betweenness centrality, and closeness centrality. The weighting strategy includes two modes: In the original mode, it enhances the network synchronizability by increasing the weights of bridge edges; whereas in the inverse version, it increases the significance of community structure by decreasing the weights of bridge edges. The scheme of weighting is controlled by only one parameter, i.e., $\alpha$ , which can be easily performed. We test the effectiveness of our model on a number of artificial benchmark networks as well as real-world ones. To the best of our knowledge, the proposed weighting strategy can outperform the existing methods in improving the performance of network computation.

Identification of influential spreaders in complex networks using HybridRank algorithm

Abstract: Identifying the influential spreaders in complex networks is crucial to understand who is responsible for the spreading processes and the influence maximization through networks. Targeting these influential spreaders is significant for designing strategies for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing methods such as local centrality measures like degree centrality are less effective, and global measures like closeness and betweenness centrality could better identify influential spreaders but they have some limitations. In this paper, we propose the HybridRank algorithm using a new hybrid centrality measure for detecting a set of influential spreaders using the topological features of the network. We use the SIR spreading model for simulating the spreading processes in networks to evaluate the performance of our algorithm. Empirical experiments are conducted on real and artificial networks, and the results show that the spreaders identified by our approach are more influential than several benchmarks.

ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages

Abstract: ABPA Ξ AΣ (ABRAXAS): Gnostic word of mystic meaning.We present ABRA, a suite of algorithms to compute and maintain probabilistically guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, ABRA is the first application of these concepts to the field of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime number of samples, and accuracy, state-of-the-art algorithms with the same quality guarantees.

Betweenness centrality in multiplex brain network during mental task evaluation

Abstract: In this paper we study the structural properties of a functional network of the human brain during the evaluation of mental tasks using the concept of betweenness centrality. We carry out the experiments involving the alternating trials of mental task evaluation with simultaneous registration of electroencephalographic (EEG) data. Using the wavelet phase coherence we reconstruct the functional multiplex network of the brain considering the different typical frequency bands of EEG activity as interconnected layers. We reveal that transition from a resting state to evaluation of a cognitive task leads to the strong outflow of shortest paths from low frequencies and strengthening of high-frequency connectivity in the brain. At the same time, we observe that mental activity shapes the shortest paths in a more uniform distribution across the brain, which implies the emergence of a more distributed functional network. Our results are in good agreement with recent studies of cognitive activity and can be implied in the design of brain-computer interfaces for the estimation of cognitive load or attention.

How leader role identity influences the process of leader emergence: A social network analysis

Abstract: Contemporary theories on leadership development emphasize the importance of having a leader identity in building leadership skills and functioning effectively as leaders. We build on this approach by unpacking the role leader identity plays in the leader emergence process. Taking the perspective that leadership is a dynamic social process between group members, we propose a social network-based process model whereby leader role identity predicts network centrality (i.e., betweenness and indegree), which then contributes to leader emergence. We test our model using a sample of 88 cadets participating in a leadership development training course. In support of our model, cadets who possess a stronger leader role identity at the beginning of the course were more likely to emerge as leaders. However this relationship was only mediated by one form of network centrality, indegree centrality, reflecting one's ability to build relationships within one's group. Implications for research and practice are discussed.

Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks

Abstract: Research into multi-layer network growth in the detection of urban dynamics provides scholars a new way to discuss the structure changing trends and related impacts. The quantitative research method is applied to examine, the network centrality, network accessibility and network community partition focusing on the upper-layer (rail network) network growth process. We based on the case study of Kuala Lumpur and found that when a rail network grows with a simple tree-like network to a more intricate form, the network diameter and the average shortest path length of multi-layer networks decrease dramatically. The network expansion ability keeps changing and more rail stations in the city centre have higher ability for future expansion. Changes in betweenness centrality and closeness centrality of multi-layer networks essentially hinge on the growth of rail network, with the highest change rate of closeness centrality at around 211.48%. The growth of network allows the remainder of the network to be easily visited, with the highest change rate of network accessibility around 12%. Different performances of these nodes added in the multi-layer network are discussed to show their impact on the repartition of network communities and the number of communities is decreasing. We believe this research can benefit scholars to easily understand and apply these network dynamic computational techniques.

Improving the Betweenness Centrality of a Node by Adding Links

Abstract: Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network. In several scenarios, having a high betweenness can have a positive impact on the node itself. Hence, in this article, we consider the problem of determining how much a vertex can increase its centrality by creating a limited amount of new edges incident to it. In particular, we study the problem of maximizing the betweenness score of a given node—Maximum Betweenness Improvement (MBI)—and that of maximizing the ranking of a given node—Maximum Ranking Improvement (MRI). We show that MBI cannot be approximated in polynomial-time within a factor (1−1/2e) and that MRI does not admit any polynomial-time constant factor approximation algorithm, both unless P=NP. We then propose a simple greedy approximation algorithm for MBI with an almost tight approximation ratio and we test its performance on several real-world networks. We experimentally show that our algorithm highly increases both the betweenness score and the ranking of a given node and that it outperforms several competitive baselines. To speed up the computation of our greedy algorithm, we also propose a new dynamic algorithm for updating the betweenness of one node after an edge insertion, which might be of independent interest. Using the dynamic algorithm, we are now able to compute an approximation of MBI on networks with up to 105 edges in most cases in a matter of seconds or a few minutes.

Centrality measure in social networks based on linear threshold model

Abstract: Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social network analysis and its applications. However, as far as we know, traditional models of influence spread have not yet been exhaustively used to define centrality measures according to the influence criteria. Most of the considered work in this topic is based on the independent cascade model. In this paper we explore the possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks. We propose a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure, the Linear Threshold Centralization (LTC). We appraise the viability of the approach through several case studies. We consider four different social networks to compare our new measures with two centrality measures based on relevance criteria and another centrality measure based on the independent cascade model. Our results show that our measures are useful for ranking actors and networks in a distinguishable way.

Identifying opinion leader nodes in online social networks with a new closeness evaluation algorithm

Abstract: In online social networks, there are some influential opinion leader nodes who can be used to accelerate the spread of positive information and suppress the diffusion of rumors. If these opinion leaders can be identified timely and correctly, there will be contributing to guide the popular opinions. The closeness is introduced for mapping the relationship between the nodes according to the different interaction types in online social network. In order to measure the impact of the information transmission between non-adjacent nodes in online social networks, a closeness evaluating algorithm of the adjacent nodes and the non-adjacent nodes is given based on the relational features between users. By using the algorithm, the closeness between the adjacent nodes and the non-adjacent nodes can obtained depending on the interaction time of nodes and the delay of their hops. Furthermore, a more accurate and efficient betweenness centrality scheme based on the optimized algorithm with the degree of closeness and the corresponding updating strategy. The opinion leader nodes should be identified more accurately and efficiently under the improved algorithm because the considering of closeness between nodes in the network. Finally, the maximum spreading experiment is done for comparing the proposed method with other existing identifying opinion leader selecting schemes based on the Independent Cascade Model. The result of experiment shows the effectiveness and practicality of the evaluating algorithm.

Systemic Risk Assessment in Complex Supply Networks

Abstract: The growth in size and complexity of supply chains has led to compounded risk exposure, which is hard to measure with existing risk management approaches. In this study, we apply the concept of systemic risk to show that centrality metrics can be used for complex supply network risk assessment. We review and select metrics, and set up an exemplary case applied to the material flow and contractual networks of Honda Acura. In the exemplary case study, geographical risk information is incorporated to selected systemic risk assessment metrics and results are compared to assessment without risk indicators in order to draw conclusions on how additional information can enhance systemic risk assessment in supply networks. Katz centrality is used to measure the node's risk spread using the World Risk Index. Authority and hub centralities are applied to measure the link risk spread using distances between geographical locations. Closeness is used to measure speed of disruption spread. Betweenness centrality is used to identify high-risk middlemen. Our results indicate that these metrics are successful in identifying vulnerabilities in network structure even in simplified cases, which risk practitioners can use to extend with historical data to gain more accurate insights into systemic risk exposure.

Ranking themes on co-word networks: Exploring the relationships among different metrics

Abstract: As network analysis methods prevail, more metrics are applied to co-word networks to reveal hot topics in a field. However, few studies have examined the relationships among these metrics. To bridge this gap, this study explores the relationships among different ranking metrics, including one frequency-based and six network-based metrics, in order to understand the impact of network structural features on ranking themes on co-word networks. We collected bibliographic data from three disciplines from Web of Science (WoS), and generated 40 simulation networks following the preferential attachment assumption. Correlation analysis on the empirical and simulated networks shows strong relationships among the metrics. Their relationships are consistent across disciplines. The metrics can be categorized into three groups according to the strength of their correlations, where Degree Centrality, H-index, and Coreness are in one group, Betweenness Centrality, Clustering Coefficient, and frequency in another, and Weighted PageRank by itself. Regression analysis on the simulation networks reveals that network topology properties, such as connectivity, sparsity, and aggregation, influence the relationships among selected metrics. In addition, when comparing the top keywords ranked by the metrics in the three disciplines, we found the metrics exhibit different discriminative capacity. Coreness and H-index may be better suited for categorizing keywords rather than ranking keywords. Findings from this study contribute to a better understanding of the relationships among different metrics and provide guidance for using them effectively in different contexts.

Who Spread That Rumor: Finding the Source of Information in Large Online Social Networks With Probabilistically Varying Internode Relationship Strengths

Abstract: We address the problem of estimating the source of a rumor in large-scale social networks. Previous works studying this problem have mainly focused on graph models with deterministic and homogenous internode relationship strengths. However, internode relationship strengths in real social networks are random. We model this uncertainty by using random, nonhomogenous edge weights on the underlying social network graph. We propose a novel two-stage algorithm that uses the modularity of the social network to locate the source of the rumor with fewer sensor nodes than other existing algorithms. We also propose a novel method to select these sensor nodes. We evaluate our algorithm using a large data set from Twitter and Sina Weibo. Real-world time series data are used to model the uncertainty in social relationship strengths. Simulations show that the proposed algorithm can determine the actual source within two hops, 69%–80% of the time, when the diameter of the networks varies between 7 and 13. Our numerical results also show that it is easier to estimate the source of a rumor when the source has higher betweenness centrality. Finally, we demonstrate that our two-stage algorithm outperforms the alternative algorithm in terms of the accuracy of localizing the source.

Weighted Betweenness Preferential Attachment: A New Mechanism Explaining Social Network Formation and Evolution.

Abstract: The dynamics of social networks is a complex process, as there are many factors which contribute to the formation and evolution of social links. While certain real-world properties are captured by the degree-driven preferential attachment model, it still cannot fully explain social network dynamics. Indeed, important properties such as dynamic community formation, link weight evolution, or degree saturation cannot be completely and simultaneously described by state of the art models. In this paper, we explore the distribution of social network parameters and centralities and argue that node degree is not the main attractor of new social links. Consequently, as node betweenness proves to be paramount to attracting new links – as well as strengthening existing links –, we propose the new Weighted Betweenness Preferential Attachment (WBPA) model, which renders quantitatively robust results on realistic network metrics. Moreover, we support our WBPA model with a socio-psychological interpretation, that offers a deeper understanding of the mechanics behind social network dynamics.

Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Abstract: Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose $i$ Central ; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. $i$ Central is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, $i$ Central scales to large graphs. We demonstrate with real datasets that the serial implementation of $i$ Central is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.

Leveraging local h-index to identify and rank influential spreaders in networks

Abstract: Identifying influential nodes in complex networks has received increasing attention for its great theoretical and practical applications in many fields. Some classical methods, such as degree centrality, betweenness centrality, closeness centrality, and coreness centrality, were reported to have some limitations in detecting influential nodes. Recently, the famous h-index was introduced to the network world to evaluate the spreading ability of the nodes. However, this method always assigns too many nodes with the same value, which leads to a resolution limit problem in distinguishing the real influences of these nodes. In this paper, we propose a local h-index centrality (LH-index) method to identify and rank influential nodes in networks. The LH-index method simultaneously takes into account of h-index values of the node itself and its neighbors, which is based on the idea that a node connecting to more influential nodes will also be influential. Experimental analysis on stochastic Susceptible–Infected–Recovered (SIR) model and several networks demonstrates the effectivity of the LH-index method in identifying influential nodes in networks.

Identifying critical edges in complex networks

Abstract: The critical edges in complex networks are extraordinary edges which play more significant role than other edges on the structure and function of networks. The research on identifying critical edges in complex networks has attracted much attention because of its theoretical significance as well as wide range of applications. Considering the topological structure of networks and the ability to disseminate information, an edge ranking algorithm BCCMOD based on cliques and paths in networks is proposed in this report. The effectiveness of the proposed method is evaluated by SIR model, susceptibility index S and the size of giant component σ and compared with well-known existing metrics such as Jaccard coefficient, Bridgeness index, Betweenness centrality and Reachability index in nine real networks. Experimental results show that the proposed method outperforms these well-known methods in identifying critical edges both in network connectivity and spreading dynamic.

Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information

Abstract: The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles, and GO information. To evaluate the performance of the TEO method, we compare it with five other methods (degree, betweenness, NC, Pec, and CowEWC) in detecting essential proteins from two different yeast PPI datasets. The simulation results show that adding GO information can effectively improve the predicted precision and that our method outperforms the others in predicting essential proteins.

Network constraints on learnability of probabilistic motor sequences

Abstract: Human learners are adept at grasping the complex relationships underlying incoming sequential input1. In the present work, we formalize complex relationships as graph structures2 derived from temporal associations3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties5 inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like or random organization. Graph nodes each represented a unique button press, and edges represented a transition between button presses. The results indicate that learning, indexed here by participants’ response times, was strongly mediated by the graph’s mesoscale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node’s number of connections (degree) and a node’s role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for the level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information. Kahn et al. show that learners capitalize on higher-order topological properties when they learn a probabilistic motor sequence based on a network traversal.

Authorship Trends, Collaboration Patterns, and Co-Authorship Networks in Lodging Studies (1990–2016)

Abstract: This study evaluates authorship trends, collaboration patterns, and co-authorship networks in lodging studies. Lodging-related articles published in academic journals from 1990–2016 were searched and about 2647 of these were analyzed using bibliometric analysis. The study findings suggest progressive growth in collaboration in the lodging articles. The characteristics of the co-authorship networks of lodging studies are not much different from those observed in other disciplines. Moreover, the lodging studies’ co-authorship networks match the properties of small-world network theory, which influences the diffusion speed of properties. Lastly, authors in the whole period were ranked based on network centrality metrics, such as degree centrality, Bonacich’s power index, closeness centrality, and betweenness centrality. As one of the first studies in this field, the research findings presented here provide specific theoretical and practical implications, with limitations, and the potential for expans...

An Efficient Immunization Strategy Using Overlapping Nodes and Its Neighborhoods

Abstract: When an epidemic occurs, it is often impossible to vaccinate the entire population due to limited amount of resources. Therefore, it is of prime interest to identify the set of influential spreaders to immunize, in order to minimize both the cost of vaccine resource and the disease spreading. While various strategies based on the network topology have been introduced, few works consider the influence of the community structure in the epidemic spreading process. Nowadays, it is clear that many real-world networks exhibit an overlapping community structure, in which nodes are allowed to belong to more than one community. Previous work shows that the numbers of communities to which a node belongs is a good measure of its epidemic influence. In this work, we address the effect of nodes in the neighborhood of the overlapping nodes on epidemics spreading. The proposed immunization strategy provides highly connected neighbors of overlapping nodes in the network to immunize. The whole process requires information only at the node level and is well suited to large-scale networks. Extensive experiments on four real-world networks of diverse nature have been performed. Comparisons with alternative local immunization strategies using the fraction of the Largest Connected Component (LCC) after immunization,show that the proposed method is much more efficient. Additionally, it compares favorably to global measures such as degree and betweenness centrality.

The Deformed Graph Laplacian and Its Applications to Network Centrality Analysis

Abstract: We introduce and study a new network centrality measure based on the concept of nonbacktracking walks, that is, walks not containing subsequences of the form uvu where u and v are any distinct connected vertices of the underlying graph. We argue that this feature can yield more meaningful rankings than traditional walk-based centrality measures. We show that the resulting Katz-style centrality measure may be computed via the so-called deformed graph Laplacian---a quadratic matrix polynomial that can be associated with any graph. By proving a range of new results about this matrix polynomial, we gain insights into the behavior of the algorithm with respect to its Katz-like parameter. The results also inform implementation issues. In particular we show that, in an appropriate limit, the new measure coincides with the nonbacktracking version of eigenvector centrality introduced by Martin, Zhang, and Newman in 2014. Rigorous analysis on star and star-like networks illustrates the benefits of the new approach,...

A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

Abstract: Measuring centrality has recently attracted increasing attention, with algorithms ranging from those that simply calculate the number of immediate neighbors and the shortest paths to those that are complicated iterative refinement processes and objective dynamical approaches. Indeed, vital nodes identification allows us to understand the roles that different nodes play in the structure of a network. However, quantifying centrality in complex networks with various topological structures is not an easy task. In this paper, we introduce a novel definition of entropy-based centrality, which can be applicable to weighted directed networks. By design, the total power of a node is divided into two parts, including its local power and its indirect power. The local power can be obtained by integrating the structural entropy, which reveals the communication activity and popularity of each node, and the interaction frequency entropy, which indicates its accessibility. In addition, the process of influence propagation can be captured by the two-hop subnetworks, resulting in the indirect power. In order to evaluate the performance of the entropy-based centrality, we use four weighted real-world networks with various instance sizes, degree distributions, and densities. Correspondingly, these networks are adolescent health, Bible, United States (US) airports, and Hep-th, respectively. Extensive analytical results demonstrate that the entropy-based centrality outperforms degree centrality, betweenness centrality, closeness centrality, and the Eigenvector centrality.