Model Selection for Stochastic Block Models

As a flexible representation for complex systems, networks (graphs) model entities and their interactions as nodes and edges. In many real-world networks, nodes divide naturally into functional communities, where nodes in the same group connect to the rest of the network in similar ways. Discovering such communities is an important part of modeling networks, as community structure offers clues to the processes which generated the graph. The stochastic block model is a popular network model based on community structures. It splits nodes into blocks, within which all nodes are stochastically equivalent in terms of how they connect to the rest of the network. As a generative model, it has a well-defined likelihood function with consistent parameter estimates. It is also highly flexible, capable of modeling a wide variety of community structures, including degree specific and overlapping communities. Performance of different block models vary under different scenarios. Picking the right model is crucial for successful network modeling. A good model choice should balance the trade-off between complexity and fit. The task of model selection is to automatically choose such a model given the data and the inference task. As a problem of wide interest, numerous statistical model selection techniques have been developed for classic independent data. Unfortunately, it has been a common mistake to use these techniques in block models without rigorous examinations of their derivations, ignoring the fact that some of the fundamental assumptions has been violated by moving into the domain of relational data sets such as networks. In this dissertation, I thoroughly exam the literature of statistical model selection techniques, including both Frequentist and Bayesian approaches. My goal is to develop principled statistical model selection criteria for block models by adapting classic methods for network data. I do this by running bootstrapping simulations with an efficient algorithm, and correcting classic model selection theories for block models based on the simulation data. The new model selection methods are verified by both synthetic and real world data sets

Higgs Pair Production: Improved Description by Matrix Element Matching

Higgs pair production is crucial for measuring the Higgs boson self-coupling. The dominant channel at hadron colliders is gluon fusion via heavy-quark loops. We present the results of a fully exclusive simulation of gluon fusion Higgs pair production based on the matrix elements for hh + 0, 1 partons including full heavy-quark loop dependence, matched to a parton shower. We examine and validate this new description by comparing it with (a) Higgs Effective Theory predictions, (b) exact hh + 0-parton sample showered by pythia, and (c) exact hh+1-parton distributions, by looking at the most relevant kinematic distributions, such as PTh, PThh, Mhh spectra, and jet rate as well. We find that matched samples provide an state-of-the-art accurate exclusive description of the final state. The relevant LHE files for Higgs pair productions at the LHC can be accessed via http://hepfarm02.phy.pku.edu.cn/foswiki/CMS/HH, which can be used for relevant experimental analysis.Comment: accepted version in Phys. Rev. D. arXiv admin note: substantial text overlap with arXiv:1110.172

The Majority Illusion in Social Networks

Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network

Discovery potential of Higgs boson pair production through 4ℓ\ell+E ⁣ ⁣/E\!\!/ final states at a 100 TeV collider

We explore the discovery potential of Higgs pair production at a 100 TeV collider via full leptonic mode. The same mode can be explored at the LHC when Higgs pair production is enhanced by new physics. We examine two types of fully leptonic final states and propose a partial reconstruction method. The reconstruction method can reconstruct some kinematic observables. It is found that the $m_{T2}$ variable determined by this reconstruction method and the reconstructed visible Higgs mass are important and crucial to discriminate the signal and background events. It is also noticed that a new variable, denoted as $\Delta m$ which is defined as the mass difference of two possible combinations, is very useful as a discriminant. We also investigate the interplay between the direct measurements of $t\bar{t} h$ couplings and other related couplings and trilinear Higgs coupling at hadron colliders and electron-positron colliders

Efficient Numerical Evaluation of Feynman Integral

Feynman loop integrals are a key ingredient for the calculation of higher order radiation effects, and are responsible for reliable and accurate theoretical prediction. We improve the efficiency of numerical integration in sector decomposition by implementing a quasi-Monte Carlo method associated with the CUDA/GPU technique. For demonstration we present the results of several Feynman integrals up to two loops in both Euclidean and physical kinematic regions in comparison with those obtained from FIESTA3. It is shown that both planar and non-planar two-loop master integrals in the physical kinematic region can be evaluated in less than half a minute with $\mathcal{O}(10^{-3})$ accuracy, which makes the direct numerical approach viable for precise investigation of higher order effects in multi-loop processes, e.g. the next-to-leading order QCD effect in Higgs pair production via gluon fusion with a finite top quark mass.Comment: 8 pages, 5 figures, published in Chinese Physics

Active Learning for Hidden Attributes in Networks

In many networks, vertices have hidden attributes, or types, that are correlated with the networks topology. If the topology is known but these attributes are not, and if learning the attributes is costly, we need a method for choosing which vertex to query in order to learn as much as possible about the attributes of the other vertices. We assume the network is generated by a stochastic block model, but we make no assumptions about its assortativity or disassortativity. We choose which vertex to query using two methods: 1) maximizing the mutual information between its attributes and those of the others (a well-known approach in active learning) and 2) maximizing the average agreement between two independent samples of the conditional Gibbs distribution. Experimental results show that both these methods do much better than simple heuristics. They also consistently identify certain vertices as important by querying them early on