Link prediction using discrete-time quantum walk

Predviđanje veze jedno je od ključnih pitanja složenih mreža koje trenutačno privlači pozor mnogih istraživača. Do sada su predložene mnoge metode predviđanja veze. Klasično slučajno gibanje predstavlja učinkoviti alat koji se uvelike rabi u proučavanju problema predviđanja veze. Kvantno gibanje je kvantni analog klasičnog slučajnog gibanja. Rezultati mnogih istraživanja pokazuju da kvantni algoritmi koji rabe kvantno gibanje nadmašujuju svoje klasične kopije u mnogim primjenama, kao što su, na primjer, usklađivanje i istraživanje grafikona. Međutim, malo je istraživanja o predviđanju veze na temelju kvantnog gibanja, a posebice kvantnog gibanja u diskretnom vremenu. U ovom se radu predlaže nova metoda predviđanja veze zasnovana na kvantnom gibanju u diskretnom vremenu. Rezultati eksperimenta pokazuju da je točnost predviđanja našom metodom bolja nego tipičnim metodama. Vremenska složenost naše metode koja se izvodi na klasičnim računalima, u usporedbi s metodama baziranim na klasičnom slučajnom gibanju, malo je bolja. No, naša se metoda može znatno ubrzati izvođenjem na kvantnim računalima.Link prediction is one of the key issues of complex networks, which attracts much research interest currently. Many link prediction methods have been proposed so far. The classical random walk as an effective tool has been widely used to study the link prediction problems. Quantum walk is the quantum analogue of classical random walk. Numerous research results show that quantum algorithms using quantum walk outperform their classical counterparts in many applications, such as graph matching and searching. But there have been few studies of the link prediction based on quantum walk, especially on discrete-time quantum walk. This paper proposes a new link prediction method based on discrete-time quantum walk. Experiment results show that prediction accuracy of our method is better than the typical methods. The time complexity of our method running on classical computers, compared with the methods based on classical random walk, is slightly higher. But our method can be greatly accelerated by executing on quantum computers

Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction

The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse problem can be solved by convex optimization method. We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization. The experimental results demonstrate that our proposed algorithm can achieve superior reconstruction quality and faster reconstruction speed in cardiac cine image compared to existing state-of-art reconstruction methods

Inferring and analysis of social networks using RFID check-in data in China.

Social networks play an important role in our daily lives. However, social ties are rather elusive to quantify, especially for large groups of subjects over prolonged periods of time. In this work, we first propose a methodology for extracting social ties from long spatio-temporal data streams, where the subjects are 17,795 undergraduates from a university of China and the data streams are the 9,147,106 time-stamped RFID check-in records left behind by them during one academic year. By several metrics mentioned below, we then analyze the structure of the social network. Our results center around three main observations. First, we characterize the global structure of the network, and we confirm the small-world phenomenon on a global scale. Second, we find that the network shows clear community structure. And we observe that younger students at lower levels tend to form large communities, while students at higher levels mostly form smaller communities. Third, we characterize the assortativity patterns by studying the basic demographic and network properties of users. We observe clear degree assortativity on a global scale. Furthermore, we find a strong effect of grade and school on tie formation preference, but we do not find any strong region homophily. Our research may help us to elucidate the structural characteristics and the preference of the formation of social ties in college students' social network

An Improved Biometric-Based User Authentication Scheme for C/S System

The authors first review the recently proposed Das's biometric-based remote user authentication scheme, and then show that Das's scheme is still insecure against some attacks and has some problems in password change phase. In order to overcome the design flaws in Das's scheme, an improvement of the scheme is further proposed. Cryptanalysis shows that our scheme is more efficient and secure against most of attacks; moreover, our scheme can provide strong mutual authentication by using verifying biometric, password as well as random nonces generated by the user and server

Degree assortativity.

<p>(a) the intra- and inter-gender degree assortativity coefficient. (b) the intra- and inter-grade degree assortativity coefficient. (c) the intra- and inter-school degree assortativity coefficient. (d) the intra- and inter-age degree assortativity coefficient. (e) the intra- and inter-region degree assortativity coefficient.</p

The correlations and hit rates between real social ties(friend ties) and inferred social ties.

<p>The correlations and hit rates between real social ties(friend ties) and inferred social ties.</p

Network components and path length.

<p>(a) The fraction of components with a given component size for the SVCN on a log-log scale. (b) The fraction of student pairs <i>p</i>(<i>l</i>) that are within <i>l</i> hops of each other.</p

The community structure of the SVCN.

<p>To better visualize the SVCN, we used Gephi to draw a force-directed graph based on the ForceAtlas2 algorithm. The color of the node identifies the grade level of the corresponding student, i.e., purple for freshman, orange for sophomore, green for junior, blue for senior respectively.</p