Personalized PageRank Clustering: A graph clustering algorithm based on random walks

Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.     

Ionization energies of beryllium in strong magnetic fields

We have develop an effective frozen core approximation to calculate energy levels and ionization enegies of the beryllium atom in magnetic field strengths up to 2.35×105T. Systematic improvement over the Hartree-Fock results for the beryllium low-lying states has been accomplished.     

Community detection in social networks based on improved Label Propagation Algorithm and balanced link density

Nowadays, community detection has been raised as one of the key research areas in the online social networks mining. One of the most common algorithms in this field is label propagation algorithm (LPA). Even though the LPA method has advantages such as simplicity in understanding and implementation, as well as linear time complexity, it has an important disadvantage of the uncertainty and instability in outcomes, that is, the algorithm detects and reports different combinations of communities in each run. This problem originates from the nature of random selection in the LPA method. In this paper, a novel method is proposed based on the LPA method and the inherent structure, that is, link density feature, of the input network. The proposed method uses a sensitivity parameter (balance parameter); by choosing the appropriate values for it, the desired qualities of the identified communities can be achieved. The proposed method is called Balanced Link Density-based Label Propagation (BLDLP). In comparison with the basic LPA, the proposed method has an advantage of certainty and stability in the output results, whereas its time complexity is still comparable with the basic LPA and of course lowers than many other approaches. The proposed method has been evaluated on real-world known datasets, such as the Facebook social network and American football clubs, and by comparing it with the basic LPA, the effectiveness of the proposed method in terms of the quality of the communities found and the time complexity has been shown.     

Efficient algorithm based on neighborhood overlap for community identification in complex networks

Community structure is an important feature in many real-world networks. Many methods and algorithms for identifying communities have been proposed and have attracted great attention in recent years. In this paper, we present a new approach for discovering the community structure in networks. The novelty is that the algorithm uses the strength of the ties for sorting out nodes into communities. More specifically, we use the principle of weak ties hypothesis to determine to what community the node belongs. The advantages of this method are its simplicity, accuracy, and low computational cost. We demonstrate the effectiveness and efficiency of our algorithm both on real-world networks and on benchmark graphs. We also show that the distribution of link strength can give a general view of the basic structure information of graphs.     

Luminescence related processes in semiconductor nanocrystals —The strong confinement regime

This paper summarizes systematic studies on the luminescence from CdSe nanocrystals in the situation of strong zero-dimensional confinement. We discuss the role of surface and defect states, the origin of the photo-darkening and demonstrate by calorimetric adsorption measurements that the internal quantum efficiency is in the range of some 10%. The lifetimes of the intrinsic excitations are strongly size-dependent. While the radiative recombination (≈ 1 ns) is dominant for larger nanocrystals, we observe a dramatic increase of the non-radiative rate below 4 nm as a result of the reduced volume to surface ratio. The combination of ultra-fast and size-selective excitation allows us to observe a progression of sharp LO-phonon lines signifying the zero-dimensional character of the nanocrystals and providing information on the strength of the phonon coupling and the homogeneous width of the quantum-confined ground state.     

A manifestly gauge-invariant description of interaction of atomic systems with strong fields in the dipole approximation

We propose a new type of gauge-invariant expansion of the ionization probability amplitudes of atoms by short pulses of electromagnetic radiation. Contrary to previous gauge-invariant approaches to this problem it does not require different partitions of the total Hamiltonian depending on the choice of gauge. In a natural way the atomic potential is treated as perturbation acting on an electron interacting with strong pulse. Whereas this is a standard assumption of strong field approximation (SFA), we show that grouping consequently together all terms of the same order in the atomic potential results in the expansion of the amplitude which is gauge invariant order by order, and not only in the limit of infinite series. In this approach, which is illustrated by numerical examples, the “direct ionization” and “rescattering” contributions are different from those commonly used in SFA-calculations.     

Local model of a scientific collaboration in physics network compared with the global model

We have constructed a collaboration network for physicists based in Iran working in different disciplines. By discussing properties like collaborators per author, shortest path, betweenness, and the concept of power in networks for this local model, and comparing with the global model, we understand how a developing country in the Middle East is contributing to the scientific growth in the world statistically. In this comparison, we found some properties of the local model which were not in accordance with the standard global society of science, which should be considered in developing the future policies. Our results show significant differences in factors like the degree and the diameter of the networks. Even though the diversity of disciplines is low in contrast with the rest of the world according to the diameter of networks, people are reluctant to collaborate as their degree shows.     

强地震的前兆次声波研究

利用在北京昌平安装的次声三点阵,对强地震前兆次声波进行了5年的连续观测。统计分析结果表明:强震前约10天内常能测到振幅很强的前兆次声。其三维动态频谱的特点是,振幅由弱逐渐加强,达到极值后变弱;先为长周期波(10-20分),紧跟着短周期波(5-10分),然后又出现更长的周期波,最后长短周期波一起出现。提前时间与三点阵到震中距离之间没有明显的关系,与震级之间也没有明显的关系。     

The dynamics of a mobile phone network

The empirical study of network dynamics has been limited by the lack of longitudinal data. Here we introduce a quantitative indicator of link persistence to explore the correlations between the structure of a mobile phone network and the persistence of its links. We show that persistent links tend to be reciprocal and are more common for people with low degree and high clustering. We study the redundancy of the associations between persistence, degree, clustering and reciprocity and show that reciprocity is the strongest predictor of tie persistence. The method presented can be easily adapted to characterize the dynamics of other networks and can be used to identify the links that are most likely to survive in the future.     

Line graphs as social networks

It was demonstrated recently that the line graphs are clustered and assortative. These topological features are known to characterize some social networks [M.E.J. Newman, Y. Park, Why social networks are different from other types of networks, Phys. Rev. E 68 (2003) 036122]; it was argued that this similarity reveals their cliquey character. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8×10⁶ people.     

Quantum kinetic theory of confined electrons in a strong time dependent field

A gauge-invariant Green’s function approach to the quantum transport of spatially confined electrons in strong electromagnetic fields is presented. The theory includes mean field and exchange effects, as well as collisions and initial correlations. It allows for a self-consistent treatment of spectral properties and collective effects (plasmons), on one hand, and nonlinear field phenomena, such as harmonic generation and multiphoton absorption, on the other. It is equally applicable to electrons in quantum dots, ultracold ions in traps and valence electrons of metal clusters.     

Optical spectra of organic quantum dots in the strong coupling regime

We analyze the emission spectrum of a single organic quantum dot coupled to a microcavity in the strong coupling regime. We take into account non-linearities arising from both exciton–exciton interactions and the Pauli exclusion principle. We apply the recently developed Pade approximation for regular truncation of Green functions to calculate the emission spectrum. We show that as the number of excitations in the quantum dot increases the Rabi doublet evolves into a more complicated triplet structure.     

Hierarchical Characterization of Complex Networks

While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work considers the concept of virtual hierarchies established around each node and the respectively defined hierarchical node degree and clustering coefficient (introduced in cond-mat/0408076), complemented by new hierarchical measurements, in order to obtain a powerful set of topological features of complex networks. The interpretation of such measurements is discussed, including an analytical study of the hierarchical node degree for random networks, and the potential of the suggested measurements for the characterization of complex networks is illustrated with respect to simulations of random, scale-free and regular network models as well as real data (airports, proteins and word associations). The enhanced characterization of the connectivity provided by the set of hierarchical measurements also allows the use of agglomerative clustering methods in order to obtain taxonomies of relationships between nodes in a network, a possibility which is also illustrated in the current article.     

The coevolution of individual economic characteristics and socioeconomic networks

The dynamics of individual characteristics of economic agents is modeled with the link structure influenced by this dynamics: links between agents with similar characteristics are more stable than those between agents with vastly different characteristics. A simple scaling law describes the number of distinct surviving characteristic realizations as a function of the number of agents and the number of possible distinct characteristics realizations. With the chosen specification, the investigated properties do not essentially differ from those found for analogous sociophysics models with a fixed network structure.     

Formation process of a strong water-repellent alumina surface by the sol-gel method

A novel strong water-repellent alumina thin film is fabricated by chemically adsorbing stearic acid (STA) layer onto the porous and roughened aluminum film coated with polyethyleneimine (PEI). The formation process and the structure of the strong water-repellent alumina film are investigated by means of contact angle measurement and atomic force microscope (AFM). Results show that the water contact angles for the alumina films increase with the increase of the immersion time in the boiling water, and meanwhile, the roughness of the alumina films increases with the dissolution of the boehmite in the boiling water. Finally, the strong water-repellent film with a high water contact angle of 139.1° is obtained when the alumina films have distinct roughened morphology with some papillary peaks and porous structure. Moreover, both the roughened structure and the hydrophobic materials of the STA endow the alumina films with the strong water-repellence.     

Weighted Fractal Networks

In this paper we define a new class of weighted complex networks sharing several properties with fractal sets, and whose topology can be completely analytically characterized in terms of the involved parameters and of the fractal dimension. General networks with fractal or hierarchical structures can be set in the proposed framework that moreover could be used to provide some answers to the widespread emergence of fractal structures in nature.     

A clustering-driven medium access control protocol for WDM star networks

Channel assignment and nodes’ service order are two key issues that have to be addressed when designing medium access control (MAC) protocols for WDM star networks. Traditional scheduling techniques consider either channel assignment or nodes’ service order issues. Furthermore, they make use of information such as data channels or receivers’ availability, without combining it with senders’ demands. This paper introduces a novel approach to message scheduling algorithms for WDM star networks, which is driven by clustering techniques. The proposed clustering driven-minimum scheduling latency (CD-MSL) scheme combines all the aforementioned information to create groups of similar source nodes on the basis of the destination nodes of their messages, aiming at rearranging nodes’ service order and improving network performance. Extensive simulation results are presented, which indicate that the proposed clustering-driven scheme leads to a significantly higher throughput-delay performance, in comparison to conventional scheduling algorithms.     

光束在强湍流区中传播的到达角起伏

 基于修正Rytov理论，导出了适用于强湍流区的无限平面波和球面波的到达角起伏方差表达式及其功率谱表达式，分析了散射盘对到达角起伏的影响。研究结果表明：导出的方差表达式在弱湍流区也适用，随着Rytov方差的增加到达角起伏趋于饱和；高频功率谱的下降速度随着散射盘尺度的增加而增加。     

Complex scale-free networks with tunable power-law exponent and clustering

We introduce a network evolution process motivated by the network of citations in the scientific literature. In each iteration of the process a node is born and directed links are created from the new node to a set of target nodes already in the network. This set includes m$m$ “ambassador” nodes and l$l$ of each ambassador’s descendants where m$m$ and l$l$ are random variables selected from any choice of distributions _pl$p_l$ and _qm$q_m$. The process mimics the tendency of authors to cite varying numbers of papers included in the bibliographies of the other papers they cite. We show that the degree distributions of the networks generated after a large number of iterations are scale-free and derive an expression for the power-law exponent. In a particular case of the model where the number of ambassadors is always the constant m$m$ and the number of selected descendants from each ambassador is the constant l$l$, the power-law exponent is (2l+1)/l$(2l+1)/l$. For this example we derive expressions for the degree distribution and clustering coefficient in terms of l$l$ and m$m$. We conclude that the proposed model can be tuned to have the same power law exponent and clustering coefficient of a broad range of the scale-free distributions that have been studied empirically.