eprintid: 2959
rev_number: 4
eprint_status: archive
userid: 321
dir: disk0/00/00/29/59
datestamp: 2018-06-07 07:02:07
lastmod: 2018-06-07 07:02:07
status_changed: 2018-06-07 07:02:07
type: article
succeeds: 2760
metadata_visibility: show
creators_name: Du, Phuong Hanh
creators_name: Pham, Hai Dang
creators_name: Nguyen, Ngoc Hoa
creators_id: hanhdp@vnu.edu.vn
creators_id: dangph@vnu.edu.vn
creators_id: hoa.nguyen@vnu.edu.vn
title: An Efficient Parallel Method for Optimizing Concurrent Operations on Social Networks
ispublished: pub
subjects: IT
subjects: Scopus
subjects: isi
divisions: fac_fit
abstract: This paper presents our approach to optimize the performance of both reading and writing concurrent operations on large-scale social network. Here, we focus on the directed, unweighted relationships among members in a social network. It can then be illustrated as a directed, unweighted graph. And determining the relationship between any two members is similar to finding the shortest path between two vertices. With such a large-scale dynamic social network, we face the problem of having concurrent operations from adding or removing edges dynamically while one may ask to determine the relationship between two members. To solve this issue, we propose an efficient parallel method based on (i) utilizing an appropriate data structure, (ii) parallelizing the updating actions and (iii) improving the performance of query processing by both reducing the searching space and computing in multi-threaded parallel. Our method was validated by the datasets from SigMod Contest 2016 and SNAP DataSet Collections with good experimental results compared to other solutions
date: 2018-04
date_type: published
publisher: Springer
full_text_status: none
publication: Transactions on Computational Collective Intelligence
volume: 29
pagerange: 182-199
refereed: FALSE
issn: 2190-9288
citation:   Du, Phuong Hanh and Pham, Hai Dang and Nguyen, Ngoc Hoa  (2018) An Efficient Parallel Method for Optimizing Concurrent Operations on Social Networks.  Transactions on Computational Collective Intelligence, 29 .   pp. 182-199.  ISSN 2190-9288