Outward Influence and Cascade Size Estimation in Billion-scale Networks

Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes $S$ will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence, and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is $\Omega(\log^4 n)$ times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201

Factors Affecting the Competitiveness of Logistics Service Enterprises: A Case Study of Dong Nai Province

Competitiveness of enterprises can maintain and improve competitive advantage in consuming products or services. Besides, enterprises expanded their consumption network through surveying experiences in the logistics service sector. Therefore, the authors surveyed 30 logistics service enterprises with 500 customers. The results showed five factors that affected the competitiveness of logistics service enterprises of Dong Nai Province with 1% significance. Finally, the article draws some recommendations to help enterprises improve the competitiveness of logistics service

Gap functions and error bounds for variational-hemivariational inequalities

In this paper we investigate the gap functions and regularized gap functions for a class of variational–hemivariational inequalities of elliptic type. First, based on regularized gap functions introduced by Yamashita and Fukushima, we establish some regularized gap functions for the variational–hemivariational inequalities. Then, the global error bounds for such inequalities in terms of regularized gap functions are derived by using the properties of the Clarke generalized gradient. Finally, an application to a stationary nonsmooth semipermeability problem is given to illustrate our main results

SYNTHESIS OF STARCH MODIFIED MONTMORILLONITE AS AN EFFECTIVE ADSORBENT FOR Pb (II) REMOVAL FROM WATER

The adsorbent is prepared by the montmorillonite co-modification with starch for the removal of Pb (II) ions from aqueous solution. The Fourier-transformed infrared (FTIR), X-ray diffraction (XRD) spectroscopies were used to determine the structure and characteristics of the adsorbent. The main factors affecting the removal of Pb (II) ions were investigated, including the effect of pH, contact time, adsorbent dosage and the initial concentration of Pb (II). Batch process can be used for adsorption and equilibrium studies. The experimental data were fitted using Freundlich and Langmuir adsorption models. The Langmuir isotherm best fitted the experimental data with R2 0.99 and maximum Pb (II) adsorption capacity of 21.5 mg/g indicated monolayer adsorption. Kinetic studies using pseudo-first-order and pseudo-second-order rate models showed that the process complied well with the pseudo second-order rate model

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.Comment: Technical report for accepted paper at WSDM 202

Result Selection and Summarization for Web Table Search

The amount of information available on the Web has been growing dramatically, raising the importance of techniques for searching the Web. Recently, Web Tables emerged as a model, which enables users to search for information in a structured way. However, effective presentation of results for Web Table search requires (1) selecting a ranking of tables that acknowledges the diversity within the search result; and (2) summarizing the information content of the selected tables concisely but meaningful. In this paper, we formalize these requirements as the \emph{diversified table selection} problem and the \emph{structured table summarization} problem. We show that both problems are computationally intractable and, thus, present heuristic algorithms to solve them. For these algorithms, we prove salient performance guarantees, such as near-optimality, stability, and fairness. Our experiments with real-world collections of thousands of Web Tables highlight the scalability of our techniques. We achieve improvements up to 50\% in diversity and 10\% in relevance over baselines for Web Table selection, and reduce the information loss induced by table summarization by up to 50\%. In a user study, we observed that our techniques are preferred over alternative solutions

Privacy-Preserving Schema Reuse

As the number of schema repositories grows rapidly and several web-based platforms exist to support publishing schemas, \emph{schema reuse} becomes a new trend. Schema reuse is a methodology that allows users to create new schemas by copying and adapting existing ones. This methodology supports to reduce not only the effort of designing new schemas but also the heterogeneity between them. One of the biggest barriers of schema reuse is about privacy concerns that discourage schema owners from contributing their schemas. Addressing this problem, we develop a framework that enables privacy-preserving schema reuse. Our framework supports the contributors to define their own protection policies in the form of \emph{privacy constraints}. Instead of showing original schemas, the framework returns an \emph{anonymized schema} with maximal \emph{utility} while satisfying these privacy constraints. To validate our approach, we empirically show the efficiency of different heuristics, the correctness of the proposed utility function, the computation time, as well as the trade-off between utility and privacy

An Evaluation of Aggregation Techniques in Crowdsourcing

As the volumes of AI problems involving human knowledge are likely to soar, crowdsourcing has become essential in a wide range of world-wide-web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from the crowd since the workers might have wide-ranging levels of expertise. In order to tackle this challenge, many aggregation techniques have been proposed. These techniques, however, have never been compared and analyzed under the same setting, rendering a `right' choice for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the aggregation techniques. Specifically, we integrated several state-of-the-art methods in a comparable manner, and measured various performance metrics with our benchmark, including \emph{computation time, accuracy, robustness to spammers,} and \emph{adaptivity to multi-labeling}. We then provide in-depth analysis of benchmarking results, obtained by simulating the crowdsourcing process with different types of workers. We believe that the findings from the benchmark will be able to serve as a practical guideline for crowdsourcing applications