Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The Pythia program can be used to generate high-energy-physics ''events'', i.e. sets of outgoing particles produced in the interactions between two incoming particles. The objective is to provide as accurate as possible a representation of event properties in a wide range of reactions, within and beyond the Standard Model, with emphasis on those where strong interactions play a role, directly or indirectly, and therefore multihadronic final states are produced. The physics is then not understood well enough to give an exact description; instead the program has to be based on a combination of analytical results and various QCD-based models. This physics input is summarized here, for areas such as hard subprocesses, initial- and final-state parton showers, underlying events and beam remnants, fragmentation and decays, and much more. Furthermore, extensive information is provided on all program elements: subroutines and functions, switches and parameters, and particle and process data. This should allow the user to tailor the generation task to the topics of interest.

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PYTHIA 6.4 Physics and Manual

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.

https://dl.acm.org/doi/pdf/10.1145/3052973.3053009

Practical Black-Box Attacks against Machine Learning

The structure and dynamics of multilayer networks

A novel modified method for obtaining approximate solutions to difficult optimization problems within the neural network paradigm is presented We consider the graph partition and the travelling salesman problems The key new ingredient is a reduction of solution space by one dimension by using graded neurons, thereby avoiding the destructive redundancy that has plagued these problems when using straightforward neural network techniques This approach maps the problems onto Potts glass rather than spin glass theories A systematic prescription is given for estimating the phase transition temperatures in advance, which facilitates the choice of optimal parameters This analysis, which is performed for both serial and synchronous updating of the mean field theory equations, makes it possible to consistently avoid chaotic behaviorWhen exploring this new technique numerically we find the results very encouraging; the quality of the solutions are in parity with those obtained by using optimally tuned simulated annealing heuristics Our numerical study, which for TSP extends to 200-city problems, exhibits an impressive level of parameter insensitivity (Less)

A new method for mapping optimization problems onto neural networks

We present a general condition on quark fragmentation which gives a hadron distribution satisfying Lorentz invariance and causality. The hadronization can be described as an iterative cascade process, symmetric with respect to iteration from the quark and the antiquark ends. The possible particle distributions are strongly restricted, with few free parameters related to the total multiplicity and corelations in rapidity. These parameters can be given an appealing interpretation in terms of the expected area and perimeter dependence of Wilson loop integrals.

A general model for jet fragmentation

We present and investigate an extension of the classical random graph to a general class of inhomogeneous random graph models, where vertices come in different types, and the probability of realizing an edge depends on the types of its terminal vertices. This approach provides a general framework for the analysis of a large class of models. The generic phase structure is derived using generating function techniques, and relations to other classes of models are pointed out.

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General formalism for inhomogeneous random graphs.

A strategy for finding approximate solutions to discrete optimization problems with inequality constraints using mean field neural networks is presented. The constraints x d 0 are encoded by x–(x) terms in the energy function. A careful treatment of the mean field approximation for the self-coupling parts of the energy is crucial, and results in an essentially parameter-free algorithm. This methodology is extensively tested on the knapsack problem of size up to 103 items. The algorithm scales like NM for problems with N items and M constraints. Comparisons are made with an exact branch and bound algorithm when this is computationally possible (N d 30). The quality of the neural network solutions consistently lies above 95% of the optimal ones at a significantly lower CPU expense. For the larger problem sizes the algorithm is compared with simulated annealing and a modified linear programming approach. For "nonhomogeneous" problems these produce good solutions, whereas for the more difficult "homogeneous" problems the neural approach is a winner with respect to solution quality and/or CPU time consumption. The approach is of course also applicable to other problems of similar structure, like set covering.

Neural networks for optimization problems with inequality constraints: the knapsack problem

The self-similar structure of mode lockings for circle maps is studied by means of the associated Farey trees. We investigate numerically several classes of scaling relations implicit in the Farey organization of mode lockings and discuss the extent to which they lead to universal scaling laws.

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Bo Söderberg

Papers

A new method for mapping optimization problems onto neural networks

A general model for jet fragmentation

General formalism for inhomogeneous random graphs.

Neural networks for optimization problems with inequality constraints: the knapsack problem

Scaling Laws for Mode Lockings in Circle Maps