"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:645096af-a8b6-4e9a-a1b0-3646b13a3074","http://resolver.tudelft.nl/uuid:645096af-a8b6-4e9a-a1b0-3646b13a3074","Structural transition in interdependent networks with regular interconnections","Wang, X. (TU Delft Network Architectures and Services); Kooij, R.E. (TU Delft Network Architectures and Services; Singapore University of Technology and Design); Moreno, Yamir (University of Zaragoza; ISI Foundation); Van Mieghem, P.F.A. (TU Delft Network Architectures and Services)","","2019","Networks are often made up of several layers that exhibit diverse degrees of interdependencies. An interdependent network consists of a set of graphs G that are interconnected through a weighted interconnection matrix B, where the weight of each intergraph link is a non-negative real number p. Various dynamical processes, such as synchronization, cascading failures in power grids, and diffusion processes, are described by the Laplacian matrix Q characterizing the whole system. For the case in which the multilayer graph is a multiplex, where the number of nodes in each layer is the same and the interconnection matrix B=pI, I being the identity matrix, it has been shown that there exists a structural transition at some critical coupling p∗. This transition is such that dynamical processes are separated into two regimes: if p>p∗, the network acts as a whole; whereas when p<p∗, the network operates as if the graphs encoding the layers were isolated. In this paper, we extend and generalize the structural transition threshold p∗ to a regular interconnection matrix B (constant row and column sum). Specifically, we provide upper and lower bounds for the transition threshold p∗ in interdependent networks with a regular interconnection matrix B and derive the exact transition threshold for special scenarios using the formalism of quotient graphs. Additionally, we discuss the physical meaning of the transition threshold p∗ in terms of the minimum cut and show, through a counterexample, that the structural transition does not always exist. Our results are one step forward on the characterization of more realistic multilayer networks and might be relevant for systems that deviate from the topological constraints imposed by multiplex networks.","","en","journal article","","","","","","","","","","","","","",""
"uuid:551f9723-8397-4ef8-8a89-36bdd422d270","http://resolver.tudelft.nl/uuid:551f9723-8397-4ef8-8a89-36bdd422d270","Ranking of Nodal Infection Probability in Susceptible-Infected-Susceptible Epidemic","Qu, B. (TU Delft Multimedia Computing); Li, C. (Fudan University); Van Mieghem, P.F.A. (TU Delft Network Architectures and Services); Wang, H. (TU Delft Multimedia Computing)","","2017","The prevalence, which is the average fraction of infected nodes, has been studied to evaluate the robustness of a network subject to the spread of epidemics. We explore the vulnerability (infection probability) of each node in the metastable state with a given effective infection rate τ. Specifically, we investigate the ranking of the nodal vulnerability subject to a susceptible-infected-susceptible epidemic, motivated by the fact that the ranking can be crucial for a network operator to assess which nodes are more vulnerable. Via both theoretical and numerical approaches, we unveil that the ranking of nodal vulnerability tends to change more significantly as τ varies when τ is smaller or in Barabási-Albert than Erdos-Rényi random graphs.","Complex networks; Statistical physics","en","journal article","","","","","","","","","","","Multimedia Computing","","",""
"uuid:1f46cc50-c3e6-425c-91bb-d05944301420","http://resolver.tudelft.nl/uuid:1f46cc50-c3e6-425c-91bb-d05944301420","Kemeny's constant and the effective graph resistance","Wang, X. (TU Delft Mathematical Physics); Dubbeldam, J.L.A. (TU Delft Mathematical Physics); Van Mieghem, P.F.A. (TU Delft Network Architectures and Services)","","2017","Kemeny's constant and its relation to the effective graph resistance has been established for regular graphs by Palacios et al. [1]. Based on the Moore–Penrose pseudo-inverse of the Laplacian matrix, we derive a new closed-form formula and deduce upper and lower bounds for the Kemeny constant. Furthermore, we generalize the relation between the Kemeny constant and the effective graph resistance for a general connected, undirected graph.","Effective graph resistance or Kirchhoff index; Kemeny constant; Moore–Penrose pseudo-inverse; Multiplicative degree-Kirchhoff index; Spectral graph theory","en","journal article","","","","","","Accepted Author Manuscript","","2019-09-22","","","Mathematical Physics","","",""
"uuid:5c80616a-87ba-478d-8428-45f807f473f3","http://resolver.tudelft.nl/uuid:5c80616a-87ba-478d-8428-45f807f473f3","A network approach for power grid robustness against cascading failures","Wang, X.; Koc, Y.; Kooij, R.E.; Van Mieghem, P.","","2015","Cascading failures are one of the main reasons for blackouts in electrical power grids. Stable power supply requires a robust design of the power grid topology. Currently, the impact of the grid structure on the grid robustness is mainly assessed by purely topological metrics, that fail to capture the fundamental properties of the electrical power grids such as power flow allocation according to Kirchhoff’s laws. This paper deploys the effective graph resistance as a metric to relate the topology of a grid to its robustness against cascading failures. Specifically, the effective graph resistance is deployed as a metric for network expansions (by means of transmission line additions) of an existing power grid. Four strategies based on network properties are investigated to optimize the effective graph resistance, accordingly to improve the robustness, of a given power grid at a low computational complexity. Experimental results suggest the existence of Braess’s paradox in power grids: bringing an additional line into the system occasionally results in decrease of the grid robustness. This paper further investigates the impact of the topology on the Braess’s paradox, and identifies specific sub-structures whose existence results in Braess’s paradox in power grids. Careful assessment of the design and expansion choices of grid topologies incorporating the insights provided by this paper optimizes the robustness of a power grid, while avoiding the Braess’s paradox in the system.","","en","conference paper","RNDM","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services","","","",""
"uuid:13da8732-07ef-48cc-a5f8-fbbd4f70b0b0","http://resolver.tudelft.nl/uuid:13da8732-07ef-48cc-a5f8-fbbd4f70b0b0","Correlation between centrality metrics and their application to the opinion model","Li, C.; Li, Q.; Van Mieghem, P.F.A.; Stanley, H.E.; Wang, H.","","2015","In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The mth-order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the betweenness, the closeness, and the components of the principal eigenvector of the adjacency matrix are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between the principal eigenvector and the 2nd-order degree mass is larger than that between the principal eigenvector and a lower order degree mass. Finally, we investigate the effect of the inflexible contrarians selected based on different centrality metrics in helping one opinion to compete with another in the inflexible contrarian opinion (ICO) model. Interestingly, we find that selecting the inflexible contrarians based on the leverage, the betweenness, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the betweenness, as well as a high centrality similarity between the leverage and the degree.","statistical and nonlinear physics","en","journal article","Springer","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Intelligent Systems","","","",""
"uuid:fae9782b-6ae5-436f-b51f-a002f0a496b2","http://resolver.tudelft.nl/uuid:fae9782b-6ae5-436f-b51f-a002f0a496b2","Algebraic Connectivity of Interdependent Networks","Martin-Hernandez, J.; Wang, H.; Van Mieghem, P.; D'Agostino, G.","","2014","The algebraic connectivity UN-1, i.e. the second smallest eigenvalue of the Laplacian matrix, plays a crucial role in dynamic phenomena such as diffusion processes, synchronization stability, and network robustness. In this work we study the algebraic connectivity in the general context of interdependent networks, or network-of-networks (NoN). The present work shows, both analytically and numerically, how the algebraic connectivity of NoNs experiences a transition. The transition is characterized by a saturation of the algebraic connectivity upon the addition of suffcient coupling links (between the two individual networks of a NoN). In practical terms, this shows that NoN topologies require only a fraction of coupling links in order to achieve optimal diffusivity. Furthermore, we observe a footprint of the transition on the properties of Fiedler's spectral bisection.","Network of Networks; Synchronization; Laplacian; Spectral Properties; System of Systems","en","journal article","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures & Services (NAS)","","","",""
"uuid:5c7cff90-73ea-48f1-a43e-650aaa4dd91b","http://resolver.tudelft.nl/uuid:5c7cff90-73ea-48f1-a43e-650aaa4dd91b","Epidemic threshold in directed networks","Li, C.; Wang, H.; Van Mieghem, P.F.A.","","2013","Epidemics have so far been mostly studied in undirected networks. However, many real-world networks, such as the online social network Twitter and the world wide web, on which information, emotion, or malware spreads, are directed networks, composed of both unidirectional links and bidirectional links. We define the directionality ? as the percentage of unidirectional links. The epidemic threshold ? c for the susceptible-infected-susceptible (SIS) epidemic is lower bounded by 1/? 1 in directed networks, where ? 1 , also called the spectral radius, is the largest eigenvalue of the adjacency matrix. In this work, we propose two algorithms to generate directed networks with a given directionality ? . The effect of ? on the spectral radius ? 1 , principal eigenvector x 1 , spectral gap (? 1 ?|? 2 |), and algebraic connectivity ? N?1 is studied. Important findings are that the spectral radius ? 1 decreases with the directionality ? , whereas the spectral gap and the algebraic connectivity increase with the directionality ? . The extent of the decrease of the spectral radius depends on both the degree distribution and the degree-degree correlation ? D. Hence, in directed networks, the epidemic threshold is larger and a random walk converges to its steady state faster than that in undirected networks with the same degree distribution.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Intelligent Systems","","","",""
"uuid:82a5fd94-7bb9-4a55-8e34-fbbaa3d81f11","http://resolver.tudelft.nl/uuid:82a5fd94-7bb9-4a55-8e34-fbbaa3d81f11","Epidemic threshold and topological structure of susceptible-infectious-susceptible epidemics in adaptive networks","Guo, D.; Trajanovski, S.; Van de Bovenkamp, R.; Wang, H.; Van Mieghem, P.F.A.","","2013","The interplay between disease dynamics on a network and the dynamics of the structure of that network characterizes many real-world systems of contacts. A continuous-time adaptive susceptible-infectious-susceptible (ASIS) model is introduced in order to investigate this interaction, where a susceptible node avoids infections by breaking its links to its infected neighbors while it enhances the connections with other susceptible nodes by creating links to them. When the initial topology of the network is a complete graph, an exact solution to the average metastable-state fraction of infected nodes is derived without resorting to any mean-field approximation. A linear scaling law of the epidemic threshold ?c as a function of the effective link-breaking rate ? is found. Furthermore, the bifurcation nature of the metastable fraction of infected nodes of the ASIS model is explained. The metastable-state topology shows high connectivity and low modularity in two regions of the ?,? plane for any effective infection rate ?>?c: (i) a “strongly adaptive” region with very high ? and (ii) a “weakly adaptive” region with very low ?. These two regions are separated from the other half-open elliptical-like regions of low connectivity and high modularity in a contour-line-like way. Our results indicate that the adaptation of the topology in response to disease dynamics suppresses the infection, while it promotes the network evolution towards a topology that exhibits assortative mixing, modularity, and a binomial-like degree distribution.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Intelligent Systems","","","",""
"uuid:5dd2ae41-b46b-4312-a3bb-8772ae117897","http://resolver.tudelft.nl/uuid:5dd2ae41-b46b-4312-a3bb-8772ae117897","Topology of molecular interaction networks","Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; De Ridder, D.","","2013","Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network 1 topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.","","en","journal article","BioMed Central","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services Group (NAS)","","","",""
"uuid:0114b1b5-0c35-4ab9-9794-27556fd0ec56","http://resolver.tudelft.nl/uuid:0114b1b5-0c35-4ab9-9794-27556fd0ec56","Effect of the interconnected network structure on the epidemic threshold","Wang, H.; Li, Q.; D'Agostino, G.; Havlin, S.; Stanley, H.E.; Van Mieghem, P.","","2013","Most real-world networks are not isolated. In order to function fully, they are interconnected with other networks, and this interconnection influences their dynamic processes. For example, when the spread of a disease involves two species, the dynamics of the spread within each species (the contact network) differs from that of the spread between the two species (the interconnected network). We model two generic interconnected networks using two adjacency matrices, A and B, in which A is a 2N×2N matrix that depicts the connectivity within each of two networks of size N, and B a 2N×2N matrix that depicts the interconnections between the two. Using an N-intertwined mean-field approximation, we determine that a critical susceptible-infected-susceptible (SIS) epidemic threshold in two interconnected networks is 1/?1(A+?B), where the infection rate is ? within each of the two individual networks and ?? in the interconnected links between the two networks and ?1(A+?B) is the largest eigenvalue of the matrix A+?B. In order to determine how the epidemic threshold is dependent upon the structure of interconnected networks, we analytically derive ?1(A+?B) using a perturbation approximation for small and large ?, the lower and upper bound for any ? as a function of the adjacency matrix of the two individual networks, and the interconnections between the two and their largest eigenvalues and eigenvectors. We verify these approximation and boundary values for ?1(A+?B) using numerical simulations, and determine how component network features affect ?1(A+?B). We note that, given two isolated networks G1 and G2 with principal eigenvectors x and y, respectively, ?1(A+?B) tends to be higher when nodes i and j with a higher eigenvector component product xiyj are interconnected. This finding suggests essential insights into ways of designing interconnected networks to be robust against epidemics.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Intelligent Systems","","","",""
"uuid:0a31b891-4f8f-4f0b-932b-de1f348838e2","http://resolver.tudelft.nl/uuid:0a31b891-4f8f-4f0b-932b-de1f348838e2","Maximum modular graphs","Trajanovski, S.; Wang, H.; Van Mieghem, P.","","2012","Modularity has been explored as an important quantitative metric for community and cluster detection in networks. Finding the maximum modularity of a given graph has been proven to be NPcomplete and therefore, several heuristic algorithms have been proposed. We investigate the problem of finding the maximum modularity of classes of graphs that have the same number of links and/or nodes and determine analytical upper bounds. Moreover, from the set of all connected graphs with a fixed number of links and/or number of nodes, we construct graphs that can attain maximum modularity, named maximum modular graphs. The maximum modularity is shown to depend on the residue obtained when the number of links is divided by the number of communities. Two applications in transportation networks and datacenters design that can benefit of maximum modular partitioning are proposed.","statistical and nonlinear physics","en","journal article","EDP Sciences/Springer","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Intelligent Systems","","","",""
"uuid:838ec651-1c5f-46b3-bd2b-34288fa6b812","http://resolver.tudelft.nl/uuid:838ec651-1c5f-46b3-bd2b-34288fa6b812","Disruption of Functional Brain Networks in Alzheimer’s Disease: What Can We Learn from Graph Spectral Analysis of Resting-State Magnetoencephalography?","De Haan, W.; Van der Flier, W.M.; Wang, H.; Van Mieghem, P.F.A.; Scheltens, P.; Stam, C.J.","","2012","In Alzheimer’s disease (AD), structural and functional brain network organization is disturbed. However, many of the present network analysis measures require a priori assumptions and methodological choices that influence outcomes and interpretations. Graph spectral analysis (GSA) is a more direct algebraic method that describes network properties, which might lead to more reliable results. In this study, GSA was applied to magnetoencephalography (MEG) data to explore functional network integrity in AD. Sensor-level resting-state MEG was performed in 18 Alzheimer patients (age 67 – 9, 6 women) and 18 healthy controls (age 66 – 9, 11 women). Weighted, undirected graphs were constructed based on functional connectivity analysis using the Synchronization likelihood, and GSA was performed with a focus on network connectivity, synchronizability, and node centrality. The main outcomes were a global loss of network connectivity and altered synchronizability in most frequency bands. Eigenvector centrality mapping confirmed the hub status of the parietal areas, and demonstrated a low centrality of the left temporal region in the theta band in AD patients that was strongly related to the mini mental state examination (global cognitive function test) score (r = 0.67, p = 0.001). Summarizing, GSA is a theoretically solid approach that is able to detect the disruption of functional network topology in AD. In addition to the previously reported overall connectivity losses and parietal area hub status, impaired network synchronizability and a clinically relevant left temporal centrality loss were found in AD patients. Our findings imply that GSA is valuable for the purpose of studying altered brain network topology and dynamics in AD.","dementia; eigenvector centrality; electrophysiology; functional connectivity; magnetoencephalography; network; neurophysiology; resting-state","en","journal article","Mary Ann Liebert","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:5ef03603-9045-4bdf-a1db-4842f2f82def","http://resolver.tudelft.nl/uuid:5ef03603-9045-4bdf-a1db-4842f2f82def","Do greedy assortativity optimization algorithms produce good results?","Winterbach, W.; De Ridder, D.; Wang, H.J.; Reinders, M.; Van Mieghem, P.","","2012","","","en","conference paper","EDP sciences","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services (NAS) Group","","","",""
"uuid:e0285ddd-675d-4546-b01c-f8f5e1c86d81","http://resolver.tudelft.nl/uuid:e0285ddd-675d-4546-b01c-f8f5e1c86d81","The correlation of metrics in complex networks with applications in functional brain networks","Li, C.; Wang, H.; De Haan, W.; Stam, C.J.; Van Mieghem, P.F.A.","","2011","An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (B´arabasi–Albert graphs, Erd¨os–R´enyi random graphs and Watts–Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös–Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.","neuronal networks (experiment); network dynamics; random graphs; networks; computational neuroscience","en","journal article","IOP/SISSA","","","","","","","2012-05-25","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:9c25390b-0a24-4672-b11f-47c4c79731ef","http://resolver.tudelft.nl/uuid:9c25390b-0a24-4672-b11f-47c4c79731ef","Assortativity of complementary graphs","Wang, H.; Winterbach, W.; Van Mieghem, P.F.A.","","2011","","","en","journal article","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:46dedfa7-d6e7-4f29-88f7-a7ea0a8b87d1","http://resolver.tudelft.nl/uuid:46dedfa7-d6e7-4f29-88f7-a7ea0a8b87d1","Decreasing the spectral radius of a graph by link removals","Van Mieghem, P.; Stevanovi?, D.; Kuipers, F.; Li, C.; Van de Bovenkamp, R.; Liu, D.; Wang, H.","","2011","The decrease of the spectral radius, an important characterizer of network dynamics, by removing links is investigated. The minimization of the spectral radius by removing m links is shown to be an NP-complete problem, which suggests considering heuristic strategies. Several greedy strategies are compared, and several bounds on the decrease of the spectral radius are derived. The strategy that removes that link l=i~j with largest product (x1)i(x1)j of the components of the eigenvector x1 belonging to the largest adjacency eigenvalue is shown to be superior to other strategies in most cases. Furthermore, a scaling law where the decrease in spectral radius is inversely proportional to the number of nodes N in the graph is deduced. Another sublinear scaling law of the decrease in spectral radius versus the number m of removed links is conjectured.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:184d1254-2bb2-4205-818e-7824de728c59","http://resolver.tudelft.nl/uuid:184d1254-2bb2-4205-818e-7824de728c59","Correlating the topology of a metabolic network with its growth capacity","Winterbach, W.; Wang, H.; Reinders, M.; Van Mieghem, P.; De Ridder, D.","","2010","","","en","conference paper","ICST","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures & Services (NAS)","","","",""
"uuid:a5e4ab47-eed4-4180-b346-da086e826a02","http://resolver.tudelft.nl/uuid:a5e4ab47-eed4-4180-b346-da086e826a02","Metabolic network destruction: Relating topology to robustness","Winterbach, W.; Wang, H.; Reinders, M.; Van Mieghem, P.; De Ridder, D.","","2010","Biological networks exhibit intriguing topological properties such as small-worldness. In this paper, we investigate whether the topology of a metabolic network is related to its robustness. We do so by perturbing a metabolic system in silico, one reaction at a time and studying the correlations between growth, as predicted by flux balance analysis, and a number of topological metrics, as computed from three network representations of the metabolic system. We find that a small number of metrics correlate with growth and that only one of the network representations stands out in terms of correlated metrics. The most correlated metrics point to the importance of hub nodes in this network: so-called ""currency metabolites"". Since they are responsible for interconnecting distant functional modules in the network, they are important points in the networks for predicting if reaction removal affects growth. Source code and data are available upon request.","metabolic networks; ux balance analysis; network topology; robustness","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services","","","",""
"uuid:7a767b6d-d235-460f-aec2-2c3013634da9","http://resolver.tudelft.nl/uuid:7a767b6d-d235-460f-aec2-2c3013634da9","Spectral graph analysis of modularity and assortativity","Van Mieghem, P.F.A.; Ge, X.; Schumm, P.; Trajanovski, S.; Wang, H.","","2010","Expressions and bounds for Newman’s modularity are presented. These results reveal conditions for or properties of the maximum modularity of a network. The influence of the spectrum of the modularity matrix on the maximum modularity is discussed. The second part of the paper investigates how the maximum modularity, the number of clusters, and the hop count of the shortest paths vary when the assortativity of the graph is changed via degree-preserving rewiring. Via simulations, we show that the maximum modularity increases, the number of clusters decreases, and the average hop count and the effective graph resistance increase with increasing assortativity.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:1c323986-1b97-4eb8-9b88-0630ee7876a5","http://resolver.tudelft.nl/uuid:1c323986-1b97-4eb8-9b88-0630ee7876a5","Emergence of modular structure in a large-scale brain network with interactions between dynamics and connectivity","Stam, C.J.; Hillebrand, A.; Wang, H.; Van Mieghem, P.","","2010","A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an electroencephalography/magnetoencephalography like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i) synchronization dependent plasticity (SDP) and (ii) growth dependent plasticity (GDP). In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions.","complex brain networks; graph theory; small-world networks; modularity; plasticity; synchronization; development; lesion","en","journal article","Frontiers Research Foundation","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures & Services (NAS)","","","",""
"uuid:d7251537-2094-4b9c-a7d7-2823f41d49f6","http://resolver.tudelft.nl/uuid:d7251537-2094-4b9c-a7d7-2823f41d49f6","Effect of tumor resection on the characteristics of functional brain networks","Wang, H.; Douw, L.; Hernández, J.M.; Reijneveld, J.C.; Stam, C.J.; Van Mieghem, P.","","2010","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.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:68e2702f-0ae1-4f7e-9002-9bb741277878","http://resolver.tudelft.nl/uuid:68e2702f-0ae1-4f7e-9002-9bb741277878","Influence of assortativity and degree-preserving rewiring on the spectra of networks","Van Mieghem, P.; Wang, H.; Ge, X.; Tang, S.; Kuipers, F.A.","","2010","Newman’s measure for (dis)assortativity, the linear degree correlation coefficient ?D, is reformulated in terms of the total number Nk of walks in the graph with k hops. This reformulation allows us to derive a new formula from which a degree-preserving rewiring algorithm is deduced, that, in each rewiring step, either increases or decreases ?D conform our desired objective. Spectral metrics (eigenvalues of graph-related matrices), especially, the largest eigenvalue ?1 of the adjacency matrix and the algebraic connectivity ?N?1 (second-smallest eigenvalue of the Laplacian) are powerful characterizers of dynamic processes on networks such as virus spreading and synchronization processes. We present various lower bounds for the largest eigenvalue ?1 of the adjacency matrix and we show, apart from some classes of graphs such as regular graphs or bipartite graphs, that the lower bounds for ?1 increase with ?D. A new upper bound for the algebraic connectivity ?N?1 decreases with ?D. Applying the degree-preserving rewiring algorithm to various real-world networks illustrates that (a) assortative degree-preserving rewiring increases ?1, but decreases ?N?1, even leading to disconnectivity of the networks in many disjoint clusters and that (b) disassortative degree-preserving rewiring decreases ?1, but increases the algebraic connectivity, at least in the initial rewirings.","","en","journal article","EDP Sciences","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:cb32eba2-0207-4ea2-855c-2047fd8fbaaa","http://resolver.tudelft.nl/uuid:cb32eba2-0207-4ea2-855c-2047fd8fbaaa","Graphs with given diameter maximizing the algebraic connectivity","Wang, H.; Kooij, R.E.; Van Mieghem, P.","","2010","We propose a class of graphs G?D(n1, n2, ..., nD+1), containing of a chain of D+1 cliques Kn1 , Kn2 , ..., KnD+1, where neighboring cliques are fully-interconnected. The class of graphs has diameter D and size N = ? 1?i?D+1ni. We prove that this class of graphs can achieve the maximal number of links, the minimum average hopcount, and more interestingly, the maximal of any Laplacian eigenvalue among all graphs with N nodes and diameter D. The algebraic connectivity is the eigenvalue of the Laplacian that has been studied most, because it features many interesting properties. We determine the graph with the largest algebraic connectivity among graphs with N nodes and diameter D ? 4. For other diameters, numerically searching for the maximum of any eigenvalue is feasible, because (a) the searching space within the class G?D(n1, n2, ..., nD+1) is much smaller than within all graphs with N nodes and diameter D; (b) we reduce the calculation of the Laplacian spectrum from a N × N to a (D +1)× (D + 1) matrix. The maximum of any Laplacian eigenvalue obtained either theoretically or by numerical searching is applied to (1) investigate the topological features of graphs that maximize different Laplacian eigenvalues; (2) study the correlation between the maximum algebraic connectivity amax(N, D) and N as well as D and (3) evaluate two upper bounds of the algebraic connectivity that are proposed in the literature.","graphs; algebraic connectivity; maximize; diameter","en","journal article","Elsevier","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures & Services (NAS)","","","",""
"uuid:a70dcd65-8114-458d-b61a-5cd8d46f54eb","http://resolver.tudelft.nl/uuid:a70dcd65-8114-458d-b61a-5cd8d46f54eb","Spectral perturbation and reconstructability of complex networks","Liu, D.; Wang, H.; Van Mieghem, P.","","2010","In recent years, many network perturbation techniques, such as topological perturbations and service perturbations, were employed to study and improve the robustness of complex networks. However, there is no general way to evaluate the network robustness. In this paper, we propose a global measure for a network, the reconstructability coefficient ?, defined as the maximum number of eigenvalues that can be removed, subject to the condition that the adjacency matrix can be reconstructed exactly. Our main finding is that a linear scaling law, E[?]=aN, seems universal in that it holds for all networks that we have studied.","","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:93bfced7-04c7-428a-97f9-4ea0bfc09d24","http://resolver.tudelft.nl/uuid:93bfced7-04c7-428a-97f9-4ea0bfc09d24","The Observable Part of a Network","Van Mieghem, P.; Wang, H.","","2009","","overlay; observability; union of shortest paths","en","journal article","IEEE/ACM","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services","","","",""
"uuid:ff66e490-db59-4e3c-b6e2-926da4f074df","http://resolver.tudelft.nl/uuid:ff66e490-db59-4e3c-b6e2-926da4f074df","Algebraic Connectivity Optimization via Link Addition","Wang, H.; Van Mieghem, P.","","2008","","algebraic connectivity; synchronization; optimization; link addition","en","conference paper","ICST","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:9426fb39-a92e-41e0-9a82-5075d08a7b35","http://resolver.tudelft.nl/uuid:9426fb39-a92e-41e0-9a82-5075d08a7b35","The Effect of Peer Selection with Hopcount or Delay Constraint on Peer-to-Peer Networking","Tang, S.; Wang, H.; Van Mieghem, P.","","2008","","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:e764aa50-bbf9-4aca-ada7-7839c0382cd0","http://resolver.tudelft.nl/uuid:e764aa50-bbf9-4aca-ada7-7839c0382cd0","Betweenness centrality in a weighted network","Wang, H.; Martin Hernandez, J.; Van Mieghem, P.","","2008","","graph theory; telecommunication network routing","en","journal article","American Physical Society","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Telecommunications","","","",""
"uuid:abb66a4a-4d08-4652-9f16-ae697c85f6cf","http://resolver.tudelft.nl/uuid:abb66a4a-4d08-4652-9f16-ae697c85f6cf","Shifting the Link Weights in Networks","Wang, H.; Van Mieghem, P.","","2007","","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:50689b82-502c-49b6-b265-311a592f1642","http://resolver.tudelft.nl/uuid:50689b82-502c-49b6-b265-311a592f1642","Constructing the Overlay Network by Tuning Link Weights","Wang, H.; Van Mieghem, P.","","2007","","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:ee8d18fb-b846-4009-baa2-67a65ffc32d3","http://resolver.tudelft.nl/uuid:ee8d18fb-b846-4009-baa2-67a65ffc32d3","A Qualitative Comparison of Power Law Generators","Martin Hernandez, J.; Kleiberg, T.; Wang, H.; Van Mieghem, P.","","2007","","network topology; internet; power law; graphs; algorithms","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:357b3fb5-8dfc-4d75-973b-4c1b6dc2d49d","http://resolver.tudelft.nl/uuid:357b3fb5-8dfc-4d75-973b-4c1b6dc2d49d","Topological Characteristics of the Dutch Road Infrastructure","Jamakovic, A.; Wang, H.; Van Mieghem, P.","","2006","","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:2b1acb2a-d4db-40ab-8cb3-d57033a47e57","http://resolver.tudelft.nl/uuid:2b1acb2a-d4db-40ab-8cb3-d57033a47e57","The Stability of Paths in a Dynamic Network","Kuipers, F.A.; Wang, H.; Van Mieghem, P.","","2005","","network dynamics; link-state update policy; shortest path; link weight perturbation; quality of service","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","","","","",""
"uuid:055f7afd-22bf-4bc5-b841-dddf31b66217","http://resolver.tudelft.nl/uuid:055f7afd-22bf-4bc5-b841-dddf31b66217","Degree and Principal Eigenvectors in Complex Networks","Li, C.; Wang, H.; Van Mieghem, P.","","","The largest eigenvalue ? 1 of the adjacency matrix powerfully characterizes dynamic processes on networks, such as virus spread and synchronization. The minimization of the spectral radius by removing a set of links (or nodes) has been shown to be an NP-complete problem. So far, the best heuristic strategy is to remove links/nodes based on the principal eigenvector corresponding to the largest eigenvalue ? 1. This motivates us to investigate properties of the principal eigenvector x 1 and its relation with the degree vector. (a) We illustrate and explain why the average E[x 1] decreases with the linear degree correlation coefficient ? D in a network with a given degree vector; (b) The difference between the principal eigenvector and the scaled degree vector is proved to be the smallest, when ?1=N2N1 , where N k is the total number walks in the network with k hops; (c) The correlation between the principal eigenvector and the degree vector decreases when the degree correlation ? D is decreased.","networks; spectral radius; principal eigenvector; degree; as-sortativity","en","conference paper","Springer","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services Group (NAS)","","","",""
"uuid:15494d54-9d42-4433-91ff-b2901438b1a2","http://resolver.tudelft.nl/uuid:15494d54-9d42-4433-91ff-b2901438b1a2","Peer Selection with Hopcount and Delay Constraint","Tang, S.; Wang, H.; Van Mieghem, P.","","","We revisit the peer selection problem of finding the most nearby peer from an initiating peer. The metrics to assess the closeness between peers are hopcount and delay, respectively. Based on a dense graph model with i.i.d regular link weight, we calculate the probability density function to reach a peer with minimum hopcount and asymptotically analyze the probability to reach a peer with the smallest delay within a group of peers. Both results suggest that a small peer group size is enough to offer an acceptable content distribution service. We also demonstrate the applicability of our model via Internet measurements.","","en","report","Delft University of Technology","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Network Architectures and Services Group","","","",""