Distributed Triangle Approximately Counting Algorithms in Simple Graph Stream

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a graph stream has been widely studied, and numerous triangle counting steaming algorithms have emerged. To improve the throughput and scalability of streaming algorithms, many researches of distributed streaming algorithms on multiple machines are studied. In this article, we first propose a framework of distributed streaming algorithm based on the Master-Worker-Aggregator architecture. The two core parts of this framework are an edge distribution strategy, which plays a key role to affect the performance, including the communication overhead and workload balance, and aggregation method, which is critical to obtain the unbiased estimations of the global and local triangle counts in a graph stream. Then, we extend the state-of-the-art centralized algorithm TRIÈST into four distributed algorithms under our framework. Compared to their competitors, experimental results show that DVHT-i is excellent in accuracy and speed, performing better than the best existing distributed streaming algorithm. DEHT-b is the fastest algorithm and has the least communication overhead. What’s more, it almost achieves absolute workload balance.

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A Tri-Attention Neural Network Model-BasedRecommendation

Complexity ◽

10.1155/2020/3857871 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Nanxin Wang ◽

Libin Yang ◽

Yu Zheng ◽

Xiaoyan Cai ◽

Xin Mei ◽

...

Keyword(s):

Neural Network ◽

Semantic Information ◽

Mutual Influence ◽

Local Information ◽

Information Network ◽

Heterogeneous Information ◽

Meta Path ◽

Global And Local ◽

Local Representations ◽

Better Than

Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems. Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored. To solve the above deficiencies, we propose a tri-attention neural network (TANN) model for recommendation task. The proposed TANN model applies the stud genetic algorithm to automatically select meta-paths at first. Then, it learns global and local representations of each node, as well as the representations of meta-paths existing in HIN. After that, a tri-attention mechanism is proposed to enhance the mutual influence among users, items, and their related meta-paths. Finally, the encoded interaction information among the user, the item, and their related meta-paths, which contain more semantic information can be used for recommendation task. Extensive experiments on the Douban Movie, MovieLens, and Yelp datasets have demonstrated the outstanding performance of the proposed approach.

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Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Algorithms ◽

10.3390/a12010003 ◽

2018 ◽

Vol 12 (1) ◽

pp. 3 ◽

Cited By ~ 2

Author(s):

Chunhe Hu ◽

Yu Xia ◽

Junguo Zhang

Keyword(s):

Path Planning ◽

Nonlinear Problems ◽

Mapping Method ◽

Initial Population ◽

Local Optimum ◽

Planning Problem ◽

Dynamic Constraints ◽

Logistic Mapping ◽

Global And Local ◽

Better Than

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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Comparative analyses of mean-variance and mean-semivariance approaches on global and local single factor market model for developed and emerging markets

International Journal of Emerging Markets ◽

10.1108/ijoem-01-2020-0110 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mehmet Emin Yildiz ◽

Yaman Omer Erzurumlu ◽

Bora Kurtulus

Keyword(s):

Emerging Markets ◽

Stock Returns ◽

Risk Measures ◽

Emerging Market ◽

Cost Of Equity ◽

Content Type ◽

The Cost ◽

Global And Local ◽

Insight Into ◽

Better Than

PurposeThe beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.Design/methodology/approachThe study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.FindingsThe results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.Research limitations/implicationsThe data are limited to the index securities as beta could be time varying.Practical implicationsResults overall provide insight into the cost of equity calculation and emerging market assets valuation.Originality/valueThe framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

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A space efficient streaming algorithm for triangle counting using the birthday paradox

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13 ◽

10.1145/2487575.2487678 ◽

2013 ◽

Cited By ~ 51

Author(s):

Madhav Jha ◽

C. Seshadhri ◽

Ali Pinar

Keyword(s):

Streaming Algorithm ◽

Triangle Counting ◽

Birthday Paradox

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Relations between global and local topology in multiple nanotube junctions

Physical Review B ◽

10.1103/physrevb.58.12671 ◽

1998 ◽

Vol 58 (19) ◽

pp. 12671-12671 ◽

Cited By ~ 45

Author(s):

Vincent H. Crespi

Keyword(s):

Global And Local ◽

Local Topology

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THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v3i3.564 ◽

2019 ◽

Vol 3 (3) ◽

pp. 320-330

Author(s):

Nuramaliyah Nuramaliyah ◽

Asep Saefuddin ◽

Muhammad Nur Aidi

Keyword(s):

Information Criterion ◽

School Enrollment ◽

Weighted Regression ◽

Participation Rates ◽

Literacy Rate ◽

Temporal Variables ◽

Result Show ◽

Enrollment Rates ◽

Global And Local ◽

Better Than

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local inﬂuences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.

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A Fast-Converging Ensemble Infilling Approach Balancing Global Exploration and Local Exploitation: The Go-Inspired Hybrid Infilling Strategy

Journal of Mechanical Design ◽

10.1115/1.4044112 ◽

2019 ◽

Vol 142 (2) ◽

Author(s):

Liye Lv ◽

Maolin Shi ◽

Xueguan Song ◽

Wei Sun ◽

Jie Zhang

Keyword(s):

Adaptive Sampling ◽

Computational Cost ◽

Expected Improvement ◽

Sampling Strategies ◽

Key Factors ◽

Engineering Problems ◽

The Mean ◽

Global And Local ◽

Probability Of Improvement ◽

Better Than

AbstractInfilling strategies have been proposed for decades and are widely used in engineering problems. It is still challenging to achieve an effective trade-off between global exploration and local exploitation. In this paper, a novel decision-making infilling strategy named the Go-inspired hybrid infilling (Go-HI) strategy is proposed. The Go-HI strategy combines multiple individual infilling strategies, such as the mean square error (MSE), expected improvement (EI), and probability of improvement (PoI) strategies. The Go-HI strategy consists of two major parts. In the first part, a tree-like structure consisting of several subtrees is built. In the second part, the decision value for each subtree is calculated using a cross-validation (CV)-based criterion. Key factors that significantly influence the performance of the Go-HI strategy, such as the number of component infilling strategies and the tree depth, are explored. Go-HI strategies with different component strategies and tree depths are investigated and also compared with four baseline adaptive sampling strategies through three numerical functions and one engineering case. Results show that the number of component infilling strategies exerts a larger influence on the global and local performance than the tree depth; the Go-HI strategy with two component strategies performs better than the ones with three; the Go-HI strategy always outperforms the three component infilling strategies and the other four benchmark strategies in global performance and robustness and saves much computational cost.

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