Pruning based Distance Sketches with Provable Guarantees on Random Graphs

Measuring the distances between vertices on graphs is one of the most fundamental components in network analysis. Since finding shortest paths requires traversing the graph, it is challenging to obtain distance information on large graphs very quickly. In this work, we present a preprocessing algorithm that is able to create landmark based distance sketches efficiently, with strong theoretical guarantees. When evaluated on a diverse set of social and information networks, our algorithm significantly improves over existing approaches by reducing the number of landmarks stored, preprocessing time, or stretch of the estimated distances. On Erd\"{o}s-R\'{e}nyi graphs and random power law graphs with degree distribution exponent $2 < \beta < 3$, our algorithm outputs an exact distance data structure with space between $\Theta(n^{5/4})$ and $\Theta(n^{3/2})$ depending on the value of $\beta$, where $n$ is the number of vertices. We complement the algorithm with tight lower bounds for Erdos-Renyi graphs and the case when $\beta$ is close to two.Comment: Full version for the conference paper to appear in The Web Conference'1

SEISMIC VULNERABILITY ANALYSIS OF CABLE-STAYED BRIDGE DURING ROTATION CONSTRUCTION

&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Due to the swivel construction, the structural redundancy of cable-stayed bridge is reduced, and its seismic vulnerability is significantly higher than that of non-swirling construction structure and its own state of formation. Therefore, it is particularly important to study the damage changes of each component and stage system during the swivel construction of cable-stayed bridge under different horizontal earthquakes. Based on the construction of Rotary Cable-stayed Bridge in Haxi Street, the calculation formula of damage exceeding probability is established based on reliability theory, and the damage calibration of cable-stayed bridge components is carried out, and the finite element model of cable-stayed bridge rotating structure is established. The vulnerable parts of the main tower and the stay cable components of the cable-stayed bridge are identified and the incremental dynamic analysis is carried out. Finally, the seismic vulnerability curves of the main tower section, the stay cable and the rotating system are established. The results of the study show that the vulnerable areas of the H-shaped bridge towers are the abrupt changes in the main tower section near the upper and lower beams, and the vulnerable diagonal cables are the long cables anchored to the beam ends and the short cables near the main tower；At the same seismic level, the damage exceedance probability of main tower vulnerable section of cable-stayed bridge under transverse earthquake is greater than that under longitudinal earthquake, the damage exceedance probability of vulnerable stay cables under transverse seismic action is less than that under longitudinal seismic action；On the premise of the same damage probability, the required ground motion intensity of the system can be reduced by 0.35g at most compared with the component；Under the same seismic intensity, the system damage probability is 6.60 % higher than the component damage probability at most. The research results have reference significance for the construction of rotating cable-stayed bridges in areas lacking seismic records

Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?

In this paper, we introduce two local graph features for missing link prediction tasks on ogbl-citation2. We define the features as Circle Features, which are borrowed from the concept of circle of friends. We propose the detailed computing formulas for the above features. Firstly, we define the first circle feature as modified swing for common graph, which comes from bipartite graph. Secondly, we define the second circle feature as bridge, which indicates the importance of two nodes for different circle of friends. In addition, we firstly propose the above features as bias to enhance graph transformer neural network, such that graph self-attention mechanism can be improved. We implement a Circled Feature aware Graph transformer (CFG) model based on SIEG network, which utilizes a double tower structure to capture both global and local structure features. Experimental results show that CFG achieves the state-of-the-art performance on dataset ogbl-citation2.Comment: 3 pages, 2 figures, 1 table, 31 references, manuscript in preparatio

The Calculation Method of Safety Degree and Its Application in Coal Mine Enterprises

In order to evaluate the situation of safety production of coal mine enterprises effectively, quantitative analysis is necessary and very important. Safety degree of coal mine enterprises based on the concept of safety degree is defined and the method of calculating quantitatively the safety degree is put forward. The validity of this method is verified by empirical research in view of micro‐ and macroanalyses. In view of micro analysis the safety degree is derived with the calculation method based on information of one coal mine. The safety degree of this coal mine went through rapid increase period, stable period, and slow increase period. Macroresearch results show that the situation of safety production of coal mine enterprises in China has significantly been improving and the level of safety degree also has been increasing year by year since 1979, the year when the policy of reform and opening began. The reasons are the advancement of technology, strengthening of safety management and education, increasing of safety investment, and perfection of policies, laws, and regulations. These achievements can provide quantitative method for assessing the status of coal mines

Graph Neural Processes for Spatio-Temporal Extrapolation

We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either use learning-based models like Neural Networks or statistical approaches like Gaussian Processes for this task. However, the former lacks uncertainty estimates and the latter fails to capture complex spatial and temporal correlations effectively. To address these issues, we propose Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously. Specifically, we first learn deterministic spatio-temporal representations by stacking layers of causal convolutions and cross-set graph neural networks. Then, we learn latent variables for target locations through vertical latent state transitions along layers and obtain extrapolations. Importantly during the transitions, we propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that aggregates contexts considering uncertainties in context data and graph structure. Extensive experiments show that STGNP has desirable properties such as uncertainty estimates and strong learning capabilities, and achieves state-of-the-art results by a clear margin.Comment: SIGKDD 202

Spin-valley locking for in-gap quantum dots in a MoS2 transistor

Spins confined to atomically-thin semiconductors are being actively explored as quantum information carriers. In transition metal dichalcogenides (TMDCs), the hexagonal crystal lattice gives rise to an additional valley degree of freedom with spin-valley locking and potentially enhanced spin life- and coherence times. However, realizing well-separated single-particle levels, and achieving transparent electrical contact to address them has remained challenging. Here, we report well-defined spin states in a few-layer MoS$_2$ transistor, characterized with a spectral resolution of $\sim{50~\mu}$eV at ${T_\textrm{el} = 150}$~mK. Ground state magnetospectroscopy confirms a finite Berry-curvature induced coupling of spin and valley, reflected in a pronounced Zeeman anisotropy, with a large out-of-plane $g$-factor of ${g_\perp \simeq 8}$. A finite in-plane $g$-factor (${g_\parallel \simeq 0.55-0.8}$) allows us to quantify spin-valley locking and estimate the spin-orbit splitting ${2\Delta_{\rm SO} \sim 100~\mu}$eV. The demonstration of spin-valley locking is an important milestone towards realizing spin-valley quantum bits.Comment: 7 pages, 3 figure