Let It Flow: A Static Method for Exploring Dynamic Graphs

Heat transfer coefficients were investigated for saturated nucleate pool boiling of binary mixtures of water and glycerin at atmospheric pressure in a wide range of concentrations and heat fluxes. Mixtures with water mass fractions from 100% to 40% were boiled on a horizontal flat copper surface at heat fluxes from about 25 up to 270kWm−2. Experiments were carried out by static and dynamic method of measurement. Results of the static method show that the impact of mixture effects on heat transfer coefficient cannot be neglected and ideal heat transfer coefficient has to be corrected for all investigated concentrations and heat fluxes. Experimental data are correlated with the empirical correlation α=0.59q0.714+0.130ωw with mean relative error of 6%. Taking mixture effects into account, data are also successfully correlated with the combination of Stephan and Abdelsalam (1980) and Schlünder (1982) correlations with mean relative error of about 15%. Recommended coefficients of Schlünder correlation C0=1 and βL=2×10−4ms−1 were found to be acceptable for all investigated mixtures. The dynamic method was developed for fast measurement of heat transfer coefficients at continuous change of composition of boiling mixture. The dynamic method was tested for water–glycerin mixtures with water mass fractions from 70% down to 35%. Results of the dynamic method were found to be comparable with the static method. For water–glycerin mixtures with higher water mass fractions, precise temperature measurements are needed.

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Hierarchical Graph Convolutional Network for Data Evaluation of Dynamic Graphs

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9377789 ◽

2020 ◽

Author(s):

Bin Wang ◽

Teruaki Hayashi ◽

Yukio Ohsawa

Keyword(s):

Data Evaluation ◽

Dynamic Graphs ◽

Convolutional Network ◽

Hierarchical Graph

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Approximate Mining of Frequent -Subgraph Patterns in Evolving Graphs

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442590 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-35

Author(s):

Muhammad Anis Uddin Nasir ◽

Cigdem Aslay ◽

Gianmarco De Francisci Morales ◽

Matteo Riondato

Keyword(s):

Sample Size ◽

State Of The Art ◽

Empirical Evaluation ◽

Connected Subgraph ◽

Dynamic Graphs ◽

High Quality ◽

Complexity Bounds ◽

Approximation Quality ◽

Uniform Sample ◽

Waiting For Godot

“Perhaps he could dance first and think afterwards, if it isn’t too much to ask him.” S. Beckett, Waiting for Godot Given a labeled graph, the collection of -vertex induced connected subgraph patterns that appear in the graph more frequently than a user-specified minimum threshold provides a compact summary of the characteristics of the graph, and finds applications ranging from biology to network science. However, finding these patterns is challenging, even more so for dynamic graphs that evolve over time, due to the streaming nature of the input and the exponential time complexity of the problem. We study this task in both incremental and fully-dynamic streaming settings, where arbitrary edges can be added or removed from the graph. We present TipTap , a suite of algorithms to compute high-quality approximations of the frequent -vertex subgraphs w.r.t. a given threshold, at any time (i.e., point of the stream), with high probability. In contrast to existing state-of-the-art solutions that require iterating over the entire set of subgraphs in the vicinity of the updated edge, TipTap operates by efficiently maintaining a uniform sample of connected -vertex subgraphs, thanks to an optimized neighborhood-exploration procedure. We provide a theoretical analysis of the proposed algorithms in terms of their unbiasedness and of the sample size needed to obtain a desired approximation quality. Our analysis relies on sample-complexity bounds that use Vapnik–Chervonenkis dimension, a key concept from statistical learning theory, which allows us to derive a sufficient sample size that is independent from the size of the graph. The results of our empirical evaluation demonstrates that TipTap returns high-quality results more efficiently and accurately than existing baselines.

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On the Tidal Prism: The Roles of Basin Extension, Bottom Friction and Inlet Cross-Section

Journal of Marine Science and Engineering ◽

10.3390/jmse9010088 ◽

2021 ◽

Vol 9 (1) ◽

pp. 88

Author(s):

Marco Petti ◽

Sara Pascolo ◽

Silvia Bosa ◽

Nadia Busetto

Keyword(s):

Cross Section ◽

Bottom Friction ◽

Static Method ◽

Tidal Prism ◽

Empirical Relationships ◽

Cross Sectional ◽

Asymptotic Regime ◽

Static Regime ◽

The One ◽

Basin Area

The prism of the Lignano tidal inlet was approximately constant over the last forty years, although the section width has halved. This has led to questions concerning the factors that most influence the tidal prism, and on the applicability of the well-known A–P relationship. A conceptual scheme of the sea–channel–lagoon system has been used to perform a sensitivity analysis of different parameters that characterize both the basin and the inlet cross-section. A 2D hydrodynamic model has been applied to evaluate the prism and compare it to the one derived by a static method, which is the basis of the analytical derivation of the A–P linkage. Three regimes have been found in the prism variability as a function of the basin extension: a linear static regime between prism and basin area; an asymptotic regime in which the prism depends only on the basin bottom friction; and an intermediate one. In addition, the roles of the inlet and channel sizes on the prism value have been investigated. The results, compared to the empirical relationships between the prism and the inlet cross-section, show that a variation in the cross-sectional area does not always corresponds to a change in tidal prism.

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