arXiv subjects

Xiao-long Wang

Publications and source records attributed to Xiao-long Wang.

3 recordsLinked to original sources

arXiv2016

Finding the magnetic center of sextupole using vibrating wire technique

In order to obtain stable and high-quality synchrotron radiation photon, the magnets in the storage ring of High Energy Photon Source(HEPS) need to have a stable support and precise positioning. The vibrating wire technique can be used to pre-align the quadrupoles and sextupoles on one girder with high precision to meet the extremely low emittance requirement of HEPS. This thesis introduces the measurement of magnetic center of sextupole using vibrating wire. According to the measurement results, the magnetic field distribution is consistent with theoretical expectation. And vibrating wire has achieved the purpose of measuring the magnetic center and has reached a certain precision.

physics.acc-ph↗

arXiv2015

Automatic exploration of structural regularities in networks

Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, not only the group number but also the certain type of structure that a network has are usually unknown in advance. To automatically explore structural regularities in complex networks, without any prior knowledge about the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory and propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to automatically explore structural regularities in networks with a stable and state-of-the-art performance.

cs.SI↗

arXiv2014

Overlapping community detection in signed networks

Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft-memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperform other state-of-the-art models at shedding light on the community detection in synthetic signed networks.

cs.SI↗