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An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective. [ABSTRACT FROM AUTHOR]

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest. [ABSTRACT FROM AUTHOR]

Identifying influential nodes is an important topic in many diverse applications, such as accelerating information propagation, controlling rumors and diseases. Many methods have been put forward to identify influential nodes in complex networks, ranging from node centrality to diffusion-based processes. However, most of the previous studies do not take into account overlapping communities in networks. In this paper, we propose an effective method based on network representation learning. The method considers not only the overlapping communities in networks, but also the network structure. Experiments on real-world networks show that the proposed method outperforms many benchmark algorithms and can be used in large-scale networks. [ABSTRACT FROM AUTHOR]

The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k− γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks). [ABSTRACT FROM AUTHOR]

This paper demonstrates a framework of processes for identifying potential witnesses of events from evidence they post to social media. The research defines original evidence models for micro-blog content sources, the relative uncertainty of different evidence types, and models for testing evidence by combination. Methods to filter and extract evidence using automated and semi-automated means are demonstrated using a Twitter case study event. Further, an implementation to test extracted evidence using Dempster Shafer Theory of Evidence are presented. The results indicate that the inclusion of evidence from micro-blog text and linked image content can increase the number of micro-bloggers identified at events, in comparison to the number of micro-bloggers identified from geotags alone. Additionally, the number of micro-bloggers that can be tested for evidence corroboration or conflict, is increased by incorporating evidence identified in their posting history. [ABSTRACT FROM AUTHOR]

Penetration attacks are one of the most serious network security threats. However, existing network defense technologies do not have the ability to entirely block the penetration behavior of intruders. Therefore, the network needs additional defenses. In this paper, a decoy chain deployment (DCD) method based on SDN+NFV is proposed to address this problem. This method considers about the security status of networks, and deploys decoy chains with the resource constraints. DCD changes the attack surface of the network and makes it difficult for intruders to discern the current state of the network. Simulation experiments and analyses show that DCD can effectively resist penetration attacks by increasing the time cost and complexity of a penetration attack. [ABSTRACT FROM AUTHOR]

The use of RFID technology in complex and distributed environments often leads to a multi-domain RFID system, in which trust establishment among entities from heterogeneous domains without past interaction or prior agreed policy, is a challenge. The current trust management mechanisms in the literature do not meet the specific requirements in multi-domain RFID systems. Therefore, this paper analyzes the special challenges on trust management in multi-domain RFID systems, and identifies the implications and the requirements of the challenges on the solutions to the trust management of multi-domain RFID systems. A multi-domain trust management model is proposed, which provides a hierarchical trust management framework include a diversity of trust evaluation and establishment approaches. The simulation results and analysis show that the proposed method has excellent ability to deal with the trust relationships, better security, and higher accuracy rate. [ABSTRACT FROM AUTHOR]

Network address shuffling is a novel moving target defense (MTD) that invalidates the address information collected by the attacker by dynamically changing or remapping the host’s network addresses. However, most network address shuffling methods are limited by the limited address space and rely on the host’s static domain name to map to its dynamic address; therefore these methods cannot effectively defend against random scanning attacks, and cannot defend against an attacker who knows the target’s domain name. In this paper, we propose a network defense method based on random domain name and address mutation (RDAM), which increases the scanning space of the attacker through a dynamic domain name method and reduces the probability that a host will be hit by an attacker scanning IP addresses using the domain name system (DNS) query list and the time window methods. Theoretical analysis and experimental results show that RDAM can defend against scanning attacks and worm propagation more effectively than general network address shuffling methods, while introducing an acceptable operational overhead. [ABSTRACT FROM AUTHOR]

As the quality of crucial entities can directly affect that of software, their identification and protection become an important premise for effective software development, management, maintenance and testing, which thus contribute to improving the software quality and its attack-defending ability. Most analysis and evaluation on important entities like codes-based static structure analysis are on the destruction of the actual software running. In this paper, from the perspective of software execution process, we proposed an approach to mine dynamic noteworthy functions (DNFM)in software execution sequences. First, according to software decompiling and tracking stack changes, the execution traces composed of a series of function addresses were acquired. Then these traces were modeled as execution sequences and then simplified so as to get simplified sequences (SFS), followed by the extraction of patterns through pattern extraction (PE) algorithm from SFS. After that, evaluating indicators inner-importance and inter-importance were designed to measure the noteworthiness of functions in DNFM algorithm. Finally, these functions were sorted by their noteworthiness. Comparison and contrast were conducted on the experiment results from two traditional complex network-based node mining methods, namely PageRank and DegreeRank. The results show that the DNFM method can mine noteworthy functions in software effectively and precisely. [ABSTRACT FROM AUTHOR]

In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. [ABSTRACT FROM AUTHOR]

Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important topological structures in brain measures from healthy and pathological subjects. However both approaches are not consistent network estimators and are sensitive to the value of the tuning parameters. Here, we propose a consistent covariance network estimator by maximising the network likelihood (MNL) which is robust to the tuning parameter. We validate the consistency of our algorithm theoretically and via a simulation study, and contrast these results against two well-known approaches: the graphical LASSO (gLASSO) and Pearson pairwise correlations (PPC) over a range of tuning parameters. The MNL algorithm had a specificity equal to and greater than 0.94 for all sample sizes in the simulation study, and the sensitivity was shown to increase as the sample size increased. The gLASSO and PPC demonstrated a specificity-sensitivity trade-off over a range of values of tuning parameters highlighting the discrepancy in the results for misspecified values. Application of the MNL algorithm to the case study data showed a loss of connections between healthy and impaired groups, and improved ability to identify between lobe connectivity in contrast to gLASSO networks. In this work, we propose the MNL algorithm as an effective approach to find covariance brain networks, which can inform the organisational features in brain-wide analyses, particularly for large sample sizes. [ABSTRACT FROM AUTHOR]

Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease–gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science. [ABSTRACT FROM AUTHOR]

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. [ABSTRACT FROM AUTHOR]

An intriguing question in evolution is what would happen if one could “replay” life’s tape. Here, we explore the following hypothesis: when replaying the tape, the details (“decorations”) of the outcomes would vary but certain “invariants” might emerge across different life-tapes sharing similar initial conditions. We use large-scale simulations of an in silico model of pre-biotic evolution called GARD (Graded Autocatalysis Replication Domain) to test this hypothesis. GARD models the temporal evolution of molecular assemblies, governed by a rates matrix (i.e. network) that biases different molecules’ likelihood of joining or leaving a dynamically growing and splitting assembly. Previous studies have shown the emergence of so called compotypes, i.e., species capable of replication and selection response. Here, we apply networks’ science to ascertain the degree to which invariants emerge across different life-tapes under GARD dynamics and whether one can predict these invariant from the chemistry specification alone (i.e. GARD’s rates network representing initial conditions). We analysed the (complex) rates’ network communities and asked whether communities are related (and how) to the emerging species under GARD’s dynamic, and found that the communities correspond to the species emerging from the simulations. Importantly, we show how to use the set of communities detected to predict species emergence without performing any simulations. The analysis developed here may impact complex systems simulations in general. [ABSTRACT FROM AUTHOR]

The identification of modules or communities in sets of related variables is a key step in the analysis and modeling of biological systems. Procedures for this identification are usually designed to allow fast analyses of very large datasets and may produce suboptimal results when these sets are of a small to moderate size. This article introduces BoCluSt, a new, somewhat more computationally intensive, community detection procedure that is based on combining a clustering algorithm with a measure of stability under bootstrap resampling. Both computer simulation and analyses of experimental data showed that BoCluSt can outperform current procedures in the identification of multiple modules in data sets with a moderate number of variables. In addition, the procedure provides users with a null distribution of results to evaluate the support for the existence of community structure in the data. BoCluSt takes individual measures for a set of variables as input, and may be a valuable and robust exploratory tool of network analysis, as it provides 1) an estimation of the best partition of variables into modules, 2) a measure of the support for the existence of modular structures, and 3) an overall description of the whole structure, which may reveal hierarchical modular situations, in which modules are composed of smaller sub-modules. [ABSTRACT FROM AUTHOR]

Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments. [ABSTRACT FROM AUTHOR]

Biased random walk has been studied extensively over the past decade especially in the transport and communication networks communities. The mean first passage time (MFPT) of a biased random walk is an important performance indicator in those domains. While the fundamental matrix approach gives precise solution to MFPT, the computation is expensive and the solution lacks interpretability. Other approaches based on the Mean Field Theory relate MFPT to the node degree alone. However, nodes with the same degree may have very different local weight distribution, which may result in vastly different MFPT. We derive an approximate bound to the MFPT of biased random walk with short relaxation time on complex network where the biases are controlled by arbitrarily assigned node weights. We show that the MFPT of a node in this general case is closely related to not only its node degree, but also its local weight distribution. The MFPTs obtained from computer simulations also agree with the new theoretical analysis. Our result enables fast estimation of MFPT, which is useful especially to differentiate between nodes that have very different local node weight distribution even though they share the same node degrees. [ABSTRACT FROM AUTHOR]