Properties Of Large-scale Networks Research Articles

Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works mainly model the IM problem as a discrete optimization problem, and use either approximate or meta-heuristic algorithms to address this problem. However, these works are hard to find a good tradeoff between effectiveness and efficiency due to the NP-hard and large-scale network properties of the IM problem. In this article, we propose an evolutionary deep reinforcement learning algorithm (called EDRL-IM) for IM in complex networks. First, EDRL-IM models the IM problem as a continuous weight parameter optimization of deep Q network (DQN). Then, it combines an evolutionary algorithm (EA) and a deep reinforcement learning algorithm (DRL) to evolve the DQN. The EA simultaneously evolves a population of individuals, and each of which represents a possible DQN and returns a solution to the IM problem through a dynamic markov node selection strategy, while the DRL integrates all information and network-specific knowledge of DQNs to accelerate their evolution. Systematic experiments on both benchmark and real-world networks show the superiority of EDRL-IM over the state-of-the-art IM methods in finding seed nodes.

A causal role for the pulvinar in coordinating task-independent cortico-cortical interactions.

The pulvinar is the largest nucleus in the primate thalamus and has topographically organized connections with multiple cortical areas, thereby forming extensive cortico-pulvino-cortical input-output loops. Neurophysiological studies have suggested a role for these transthalamic pathways in regulating information transmission between cortical areas. However, evidence for a causal role of the pulvinar in regulating cortico-cortical interactions is sparse and it is not known whether pulvinar's influences on cortical networks are task-dependent or, alternatively, reflect more basic large-scale network properties that maintain functional connectivity across networks regardless of active task demands. In the current study, under passive viewing conditions, we conducted simultaneous electrophysiological recordings from ventral (area V4) and dorsal (lateral intraparietal area [LIP]) nodes of macaque visual system, while reversibly inactivating the dorsal part of the lateral pulvinar (dPL), which shares common anatomical connectivity with V4 and LIP, to probe a causal role of the pulvinar. Our results show a significant reduction in local field potential phase coherence between LIP and V4 in low frequencies (4-15 Hz) following muscimol injection into dPL. At the local level, no significant changes in firing rates or LFP power were observed in LIP or in V4 following dPL inactivation. Synchronization between pulvinar spikes and cortical LFP phase decreased in low frequencies (4-15 Hz) both in LIP and V4, while the low frequency synchronization between LIP spikes and pulvinar phase increased. These results indicate a causal role for pulvinar in synchronizing neural activity between interconnected cortical nodes of a large-scale network, even in the absence of an active task state.

The connectome predicts resting-state functional connectivity across the Drosophila brain

Anatomical connectivity can constrain both a neural circuit's function and its underlying computation. This principle has been demonstrated for many small, defined neural circuits. For example, connectome reconstructions have informed models for direction selectivity in the vertebrate retina1,2 as well as the Drosophila visual system.3 In these cases, the circuit in question is relatively compact, well-defined, and has known functions. However, how the connectome constrains global properties of large-scale networks, across multiple brain regions or the entire brain, is incompletely understood. As the availability of partial or complete connectomes expands to more systems and species4-8 it becomes critical to understand how this detailed anatomical information can inform our understanding of large-scale circuit function.9,10 Here, we use data from the Drosophila connectome4 in conjunction with whole-brain invivo imaging11 to relate structural and functional connectivity in the central brain. We find a strong relationship between resting-state functional correlations and direct region-to-region structural connectivity. We find that the relationship between structure and function varies across the brain, with some regions displaying a tight correspondence between structural and functional connectivity whereas others, including the mushroom body, are more strongly dependent on indirect connections. Throughout this work, we observe features of structural and functional networks in Drosophila that are strikingly similar to those seen in mammalian cortex, including in the human brain. Given the vast anatomical and functional differences between Drosophila and mammalian nervous systems, these observations suggest general principles that govern brain structure, function, and the relationship between the two.

Contrasting Web Robot and Human Behaviors with Network Models

The web graph is a commonly-used network representation of the hyperlink structure of a website. A network of similar structure to the web graph, which we call the session graph has properties that reflect the browsing habits of the agents in the web server logs. In this paper, we apply session graphs to compare the activity of humans against web robots or crawlers. Understanding these properties will enable us to improve models of HTTP traffic, which can be used to predict and generate realistic traffic for testing and improving web server efficiency, as well as devising new caching algorithms. We apply large-scale network properties, such as the connectivity and degree distribution of human and Web robot session graphs in order to identify characteristics of the traffic which would be useful for modeling web traffic and improving cache performance. We find that the empirical degree distributions of session graphs for human and robot requests on one Web server are best fit by different theoretical distributions, indicating at a difference in the processes which generate the traffic.

The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model.

We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes.

Evolution of semilocal string networks: Large-scale properties

We report on a detailed numerical study of the evolution of semilocal string networks, based on the largest and most accurate field theory simulations of these objects to date. We focus on the large-scale network properties, confirming earlier indications (coming from smaller simulations) that linear scaling is the attractor solution for the entire parameter space of initial conditions that we are able to probe. We also provide a brief comparison of our numerical results with the predictions of a previously developed one-scale model for the overall evolution of these networks. Two subsequent papers will discuss in more detail the analytic modeling of the semilocal segment populations as well as optimized numerical diagnostics.

Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data

Human brain maturation is characterized by the prolonged development of structural and functional properties of large-scale networks that extends into adulthood. However, it is not clearly understood which features change and which remain stable over time. Here, we examined structural connectivity based on diffusion tensor imaging (DTI) in 121 participants between 4 and 40 years of age. DTI data were analyzed for small-world parameters, modularity, and the number of fiber tracts at the level of streamlines. First, our findings showed that the number of fiber tracts, small-world topology, and modular organization remained largely stable despite a substantial overall decrease in the number of streamlines with age. Second, this decrease mainly affected fiber tracts that had a large number of streamlines, were short, within modules and within hemispheres; such connections were affected significantly more often than would be expected given their number of occurrences in the network. Third, streamline loss occurred earlier in females than in males. In summary, our findings suggest that core properties of structural brain connectivity, such as the small-world and modular organization, remain stable during brain maturation by focusing streamline loss to specific types of fiber tracts.

Structural Analysis of Laplacian Spectral Properties of Large-Scale Networks

Using methods from algebraic graph theory and convex optimization, we study the relationship between local structural features of a network and the eigenvalues of its Laplacian matrix. In particular, we propose a series of semidefinite programs to find new bounds on the spectral radius and the spectral gap of the Laplacian matrix in terms of a collection of local structural features of the network. Our analysis shows that the Laplacian spectral radius is strongly constrained by local structural features. On the other hand, we illustrate how local structural features are usually insufficient to accurately estimate the Laplacian spectral gap. As a consequence, random graph models in which only local structural features are prescribed are, in general, inadequate to faithfully model Laplacian spectral properties of a network.

Building Smaller Sized Surrogate Models of Complex Bipartite Networks Based on Degree Distributions

This paper presents an approach for generating surrogate bipartite networks with varying sizes based on degree distributions of given bipartite networks. The resulting surrogate networks can be used for problems such as design of algorithms for similarity search, community detection and clustering, and recommender systems. The primary advantage of using smaller surrogate networks over original large-scale networks is the reduction in associated computational expense. Degree distribution is chosen because of its widespread acceptance, simplicity, and prior literature suggesting its ability to better capture large-scale network properties. The approach is illustrated using a bipartite network from an open-source software development repository. The network consists of nodes representing people and projects, and edges representing people working on different projects. A comparison between the surrogate networks and the original networks is presented. The results show that the resized networks obtained using the proposed approach can be used to match the original degree distribution. A comparison of seven other network characteristics is also provided.

Network Analysis Reveals Increased Integration during Emotional and Motivational Processing

In recent years, a large number of human studies have investigated large-scale network properties of the brain, typically during the resting state. A critical gap in the knowledge base concerns the understanding of network properties of a focused set of brain regions during task conditions engaging these regions. Although emotion and motivation recruit many brain regions, it is currently unknown how they affect network-level properties of inter-region interactions. In the present study, we sought to characterize network structure during "mini-states" engendered by emotional and motivational cues investigated in separate studies. To do so, we used graph-theoretic network analysis to probe network-, community-, and node-level properties of the trial-by-trial functional connectivity between regions of interest. We used methods that operate on weighted graphs that make use of the continuous information of connectivity strength. In both the emotion and motivation datasets, global efficiency increased and decomposability decreased. Thus, processing became less segregated with the context signaled by the cue (potential shock or potential reward). Our findings also revealed several important features of inter-community communication, including notable contributions of the bed nucleus of the stria terminalis, anterior insula, and thalamus during threat and of the caudate and nucleus accumbens during reward. Together, the results suggest that one way in which emotional and motivational processing affect brain responses is by enhancing signal communication between regions, especially between cortical and subcortical ones.

Large-scale curvature of networks

Understanding key structural properties of large-scale networks is crucial for analyzing and optimizing their performance and improving their reliability and security. Here, through an analysis of a collection of data networks across the globe as measured and documented by previous researchers, we show that communications networks at the Internet protocol (IP) layer possess global negative curvature. We show that negative curvature is independent of previously studied network properties, and that it has a major impact on core congestion: the load at the core of a finite negatively curved network with N nodes scales as N(2), as compared to N(1.5) for a generic finite flat network.

New Benchmarks for Large-Scale Networks with Given Maximum Degree and Diameter

Large-scale networks have become ubiquitous elements of our society. Modern social networks, supported by communication and travel technology, have grown in size and complexity to unprecedented scales. Computer networks, such as the Internet, have a fundamental impact on commerce, politics and culture. The study of networks is also central in biology, chemistry and other natural sciences. Unifying aspects of these networks are a small maximum degree and a small diameter, which are also shared by many network models, such as small-world networks. Graph theoretical methodologies can be instrumental in the challenging task of predicting, constructing and studying the properties of large-scale networks. This task is now necessitated by the vulnerability of large networks to phenomena such as cross-continental spread of disease and botnets (networks of malware). In this article, we produce the new largest known networks of maximum degree 17 ≤ Δ ≤ 20 and diameter 2 ≤ D ≤ 10, using a wide range of techniques and concepts, such as graph compounding, vertex duplication, Kronecker product, polarity graphs and voltage graphs. In this way, we provide new benchmarks for networks with given maximum degree and diameter, and a complete overview of state-of-the-art methodology that can be used to construct such networks.

A graph spectral analysis of the structural similarity network of protein chains

We present a simple method for the analysis of large networks based on their graph spectral properties. One of the advantages of this method is that it uses a single numerical computation to identify subclusters in a connected graph, which can significantly simplify the complexity involved in analyzing large graphs. This is illustrated using a network of protein chains constructed on the basis of their structural similarities. The large-scale network properties and the cluster and subcluster organization of the protein chain network are presented. We summarize the results of structural and functional analyses of the nodes present in these clusters and elucidate the implications of structural similarity in the protein chain universe.

Routing with end-to-end QoS guarantees in broadband networks

We consider routing schemes for connections with end-to-end delay requirements, and investigate several fundamental problems. First, we focus on networks which employ rate-based schedulers and, hence, map delay guarantees into nodal rate guarantees, as done with the guaranteed service class proposed for the Internet. We consider first the basic problem of identifying a feasible route for the connection, for which a straightforward yet computationally costly solution exists. Accordingly, we establish several approximation schemes that offer substantially lower computational complexity. We then consider the more general problem of optimizing the route choice in terms of balancing loads and accommodating multiple connections, for which we formulate and validate several optimal algorithms. We discuss the implementation of such schemes in the context of link-state and distance-vector protocols. Next, we consider the fundamental problem of constrained path optimization. This problem, typical of quality of service routing, is NP-hard. While standard approximation methods exist, their complexity may often be prohibitive in terms of scalability. Such approximations do not make use of the particular properties of large-scale networks, such as the face that the path selection process is typically presented with a hierarchical, aggregated topology. By exploiting the structure of such topologies, we obtain an /spl epsiv/-optimal algorithm for the constrained shortest-path problem, which offers a substantial improvement in terms of scalability.