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BEGIN:VEVENT
DTSTAMP:20211207T055352Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211117T083000
DTEND;TZID=America/Chicago:20211117T170000
UID:submissions.supercomputing.org_SC21_sess255_drs111@linklings.com
SUMMARY:Parallel Algorithms for Scalable Graph Mining :  Applications on B
 ig Data and Machine Learning
DESCRIPTION:Doctoral Showcase, Posters\n\nParallel Algorithms for Scalable
  Graph Mining :  Applications on Big Data and Machine Learning\n\nSattar, 
 Arifuzzaman\n\nParallel computing plays a crucial role in processing large
 -scale graph data. Complex network analysis is an exciting area of researc
 h for many applications in different scientific domains, e.g., sociology, 
 biology, online media, recommendation systems, and many more. Machine/Deep
  learning plays a significant role in working with big data in modern era.
  We work on a well-known graph problem, community detection (CD). We desig
 n parallel algorithms for Louvain method for static networks and show arou
 nd 12-fold speedup. We also detect temporal communities in dynamic network
 s representing social/brain/communication/citation networks in a more conc
 rete way. We present a shared-memory parallel algorithm for CD in dynamic 
 graphs using permanence, a vertex-based metric. We also show the change of
  communities in different time phases computing several graph metrics base
 d on their temporal definition. We present a scalable method for CD based 
 on Graph Convolutional Network (GCN) via semi-supervised node classificati
 on using PyTorch with CUDA on GPU environment (4x performance gain). 
  Our model achieves up to 86.9% accuracy and 0.85 F1 Score on different re
 al-world datasets from diverse domains. To extend our work on deep learnin
 g, we provide a scalable solution to the Sparse Deep Neural Network (DNN) 
 Challenge by designing data parallel Sparse DNN using TensorFlow on GPU (4
 .7x speedup). We include the applications of webspam detection from webgra
 phs (billions of edges) and sentiment analysis on social network, Twitter 
 (1.2 million tweets) to reveal insights about COVID-19 vaccination awarene
 ss among the public to portray the importance of graph mining in our daily
  activities.\n\nTag: In-Person Only\n\nRegistration Category: Tech Program
  Reg Pass, Exhibit Hall Only
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