Cascade-aware partitioning of large graph databases

Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database may trigger further operations. For example, the query workload of a social network application may contain re-sharing operations in the form of cascades. It is beneficial to include the potential cascades in the graph partitioning objectives. In this paper, we introduce the problem of cascade-aware graph partitioning that aims to minimize the overall cost of communication among parts/servers during cascade processes. We develop a randomized solution that estimates the underlying cascades, and use it as an input for partitioning of large-scale graphs. Experiments on 17 real social networks demonstrate the effectiveness of the proposed solution in terms of the partitioning objectives

Scalable Graph Convolutional Network Training on Distributed-Memory Systems

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.Comment: To appear in PVLDB'2

Quality of life in type II diabetic patients in primary health care

INTRODUCTION: This study evaluated the quality of life of patients with type II diabetes in primary health care with the Turkish version of the Audit of Diabetes Dependent Quality of Life (ADDQoL) instrument. MATERIAL AND METHODS: A total of 180 patients diagnosed with type II diabetes and registered at an urban primary health care unit in Turkey were included to this study. RESULTS: The ADDQoL instrument showed good internal consistency and factor structure. Diabetes had the largest impact on "enjoyment of food" (mean impact rating -1.65) and the least impact on "others fussing" (-0.44). The duration of diabetes and insulin therapy had a significant impact on quality of life among diabetic patients. CONCLUSION: Multidimensional assessments of quality of life including both generic and disease-specific measures are important for diabetic patients in primary health care

Association between health literacy and medication adherence in the elderly population with chronic disease

Background: Medication adherence is a key factor in the therapy of chronic diseases in older people. It is important to investigate the effect of health literacy on medication adherence in this patient population. Health literacy can be summarized as an individual’s ability to understand and interpret the provided medical information and to behave appropriately based on this information. Aim: The purpose of this study was to investigate the association between health literacy and medication adherence in older people with chronic disease. Methods: A total of 175 patients admitted to the family health center clinic in Bursa, Turkey, who were older than 65 years old, were enrolled in this cross-sectional study using the convenience sampling method. A priori power analysis was conducted to determine the required sample size to reach 90% power. The Turkish version of the 8-item Morisky Medication Adherence Scale (MMAS-8) was used to assess medication adherence. The European Health Literacy Survey Questionnaire (HLS-EU-Q47) was used to evaluate health literacy. Disability associated with dyspnea was assessed using the Medical Research Council (MRC) dyspnea scale. Results: The data showed that, according to dyspnea status and diagnosis, medication adherence varied. In this sample, medication adherence in elderly patients was not associated with health literacy. Instead, medication adherence was associated with the patient’s disability and the course of the disease. Discussion and conclusions: Improving health literacy may enhance the medication adherence of older people with chronic disease. The development, practice and evaluation of health literacy interventions for older people with chronic conditions are important to increase medication adherence and potentially improve patient outcomes. [Ethiop. J. Health Dev. 2020; 34(2):90-96] Key words: Health literacy, medication adherence, older patients, chronic disease, dyspne