TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs

Given a large graph, how can we determine similarity between nodes in a fast and accurate way? Random walk with restart (RWR) is a popular measure for this purpose and has been exploited in numerous data mining applications including ranking, anomaly detection, link prediction, and community detection. However, previous methods for computing exact RWR require prohibitive storage sizes and computational costs, and alternative methods which avoid such costs by computing approximate RWR have limited accuracy. In this paper, we propose TPA, a fast, scalable, and highly accurate method for computing approximate RWR on large graphs. TPA exploits two important properties in RWR: 1) nodes close to a seed node are likely to be revisited in following steps due to block-wise structure of many real-world graphs, and 2) RWR scores of nodes which reside far from the seed node are proportional to their PageRank scores. Based on these two properties, TPA divides approximate RWR problem into two subproblems called neighbor approximation and stranger approximation. In the neighbor approximation, TPA estimates RWR scores of nodes close to the seed based on scores of few early steps from the seed. In the stranger approximation, TPA estimates RWR scores for nodes far from the seed using their PageRank. The stranger and neighbor approximations are conducted in the preprocessing phase and the online phase, respectively. Through extensive experiments, we show that TPA requires up to 3.5x less time with up to 40x less memory space than other state-of-the-art methods for the preprocessing phase. In the online phase, TPA computes approximate RWR up to 30x faster than existing methods while maintaining high accuracy.Comment: 12pages, 10 figure

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Given a time-evolving graph, how can we track similarity between nodes in a fast and accurate way, with theoretical guarantees on the convergence and the error? Random Walk with Restart (RWR) is a popular measure to estimate the similarity between nodes and has been exploited in numerous applications. Many real-world graphs are dynamic with frequent insertion/deletion of edges; thus, tracking RWR scores on dynamic graphs in an efficient way has aroused much interest among data mining researchers. Recently, dynamic RWR models based on the propagation of scores across a given graph have been proposed, and have succeeded in outperforming previous other approaches to compute RWR dynamically. However, those models fail to guarantee exactness and convergence time for updating RWR in a generalized form. In this paper, we propose OSP, a fast and accurate algorithm for computing dynamic RWR with insertion/deletion of nodes/edges in a directed/undirected graph. When the graph is updated, OSP first calculates offset scores around the modified edges, propagates the offset scores across the updated graph, and then merges them with the current RWR scores to get updated RWR scores. We prove the exactness of OSP and introduce OSP-T, a version of OSP which regulates a trade-off between accuracy and computation time by using error tolerance {\epsilon}. Given restart probability c, OSP-T guarantees to return RWR scores with O ({\epsilon} /c ) error in O (log ({\epsilon}/2)/log(1-c)) iterations. Through extensive experiments, we show that OSP tracks RWR exactly up to 4605x faster than existing static RWR method on dynamic graphs, and OSP-T requires up to 15x less time with 730x lower L1 norm error and 3.3x lower rank error than other state-of-the-art dynamic RWR methods.Comment: 10 pages, 8 figure

B2B in Asian chemical industry

Thesis(Master) --KDI School:Master of Public Policy,2001Maximized profit with minimal input; this is the foremost and eternal goal of business sector in capitalized society. Internet-savvy business people, in 21st century, have sought for the solution of continuous hunger for achieving efficiency in workflow and cost saving in the networked economy. Reducing marketing cost, saving telecommunication expenses, expanding new opportunities to new customers, accomplishing price transparency, and most importantly, controlling the whole process of business flow from production, sales to payment through networking individual companies' back-end system were believed to be finally achieved through a magic tool, internet business. Unlike people's expectation, however, the B2B concept itself is unfamiliar to most of the people and employing this new tool in such a conservative industry as chemical industry is being challenged in many facets. Many start-ups have been sprouted, but couldn't survive long. Most surviving B2Bs are also struggling and continuously being tested about their viability. New business models are continuously emerging without a strong confidence. We will see how this new industry would evolve, whether or not this would be developed as a powerful tool to be able to replace the inefficient offline trading practices after all.I. Introduction 1 II. Overview of Chemical B2B 4 III. Chemical B2B in Asia 14 IV. Strategy for chemical B2Bs in Asia 21 V. Case Study of Asian Chemical B2B: ChemCross.com 23. ConclusionmasterpublishedKang Minji

Gender-common and gender-specific determinants of child dietary diversity in eight Asia Pacific countries

Background Optimal child feeding practices contribute to reducing child undernutrition in low- and middle-income countries. Minimum dietary diversity (MDD) is a key indicator of complementary feeding quality for children aged 6-23 months. We aimed to examine the gender-common and gender-specific factors associated with the failure to meet MDD in eight Asia Pacific countries. Methods The study used data of children aged 6-23 months from the Demographic and Health Surveys (DHS) conducted in Afghanistan (n = 8410), Bangladesh (n = 2371), Nepal (n = 1478), Pakistan (n = 3490), Cambodia (n = 2182), Indonesia (n = 5133), Myanmar (n = 1379), and Timor-Leste (n = 2115). A total of 41 household, maternal, and child-level variables were examined for association with MDD using univariate and multivariable logistic regressions. All analyses accounted for the survey design and sampling weights. Results Being aged 6-11 months, not receiving Vitamin A supplementation, low maternal education, belonging to a low wealth quintile, and having two or more young children in the household were factors related to the failure to meet MDD among both male and female children. Mothers’ not watching TV or not being exposed to media at least once a week, delivery at home, young age, and engagement to non-agricultural work were only significant risk factors among female children. Non-professional delivery assistance, unsafe disposal of children’s stool, tolerant attitudes towards domestic violence, and rural residence were significant factors only among male children. Conclusions It is possible that male and female children in the region may consume food in various ways, because the factors for meeting MDD are not the same for different genders of children. It is advised to enhance dietary diversity in child nutrition programmes through gender-specific activities

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.Comment: Published at IEEE Acces

The Automatic Generation of Contextual Questions and Answers for English Learners

Understanding context is essential for ESL (English as a Second Language) students to become skilled in English. While there is an abundance of extant contextual questions, they are not tailored to ESL teachers’ course objectives and reading materials. For this reason, ESL teachers must continuously create their own contextual questions. The NLP question and answer generation tasks can lift ESL teachers’ workload by creating MCQs (Multiple Choice Questions), T/F (True or False) questions, and fill-in-the-blank questions, along with answers. We deployed a model which automatically generates MC and Wh- questions with answers. We display several examples and explain the process for generating MC and Wh- questions and answers. For our research methods, we first performed text preprocessing with the CoNLL-2014 and BEA-2019 datasets, which consist of essays written by native and non-native English students. After that, we deployed GPT-2, BERT, and T5 in order to complete the question and answer generation task. The contextual question and answer generation model will contribute specifically to ESL teachers who manually create MC and Wh- questions for ESL students, as well as to the fields of education, digital humanities, and computer science. In addition, we share tutorials for this task with the public so that anyone can make use of our research

¿Educación y salud : los sectores del futuro?

Tres de cada cuatro profesionales de la educación y la salud en la región son mujeres. De todas las profesionales que hay en la región, el 22% trabaja en el sector social. Al igual que en otras regiones, las mujeres ganan menos que los hombres. Sin embargo, la brecha salarial de género entre los trabajadores con educación postsecundaria es menor en los sectores sociales (alrededor del 10%) que en otras ocupaciones (28% en promedio). En resumen, los empleos en educación y salud son empleos de buena calidad, especialmente para mujeres. Y van a seguir creciendo. Las proyecciones indican que, bajo supuestos razonables, la región necesitará 10,3 millones de maestros, 2,4 millones de médicos y 6,2 millones de enfermeros en los próximos 15 años. Es decir, el empleo de los profesionales de la educación y la salud casi se duplicará