posted by organizer: smaz76 || 6450 views || tracked by 2 users: [display]

BigGraphs 2019 : Sixth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining

FacebookTwitterLinkedInGoogle

Link: https://biggraphs.org/
 
When Dec 9, 2019 - Dec 12, 2019
Where Los Angeles, CA, USA
Submission Deadline Oct 8, 2019
Notification Due Nov 1, 2019
Final Version Due Nov 15, 2019
Categories    computer science   big data   data mining   graph mining
 

Call For Papers

Sixth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining 9-12 December 2019

To be held in conjunction with the

2019 IEEE International Conference on Big Data (IEEE BigData 2019)
Los Angeles, CA, USA

Workshop Description
Modern Big Data increasingly appears in the form of complex graphs and networks. Examples include the physical Internet, the world wide web, online social networks, phone networks, and biological networks. In addition to their massive sizes, these graphs are dynamic, noisy, and sometimes transient. They also conform to all five Vs (Volume, Velocity, Variety, Value and Veracity) that define Big Data. However, many graph-related problems are computationally difficult, and thus big graph data brings unique challenges, as well as numerous opportunities for researchers, to solve various problems that are significant to our communities.

Big graph problems are currently solved using several complementary paradigms. The most popular approach is perhaps by exploiting parallelism, through specialized algorithms for supercomputers, shared-memory multicore and manycore systems, and heterogeneous CPU-GPU systems. However, since real-world graphs are sparse and highly irregular, there are very few parallel implementations that can actually deliver high performance. The major challenges to scaling and efficiency include irregular data dependencies, poor locality, and high synchronization costs of current approaches. In addition to parallelism, researchers are developing approximation algorithms that use sampling for compressing and summarizing graph data. Streaming algorithms are also being considered for scenarios where the rate of updates is too fast to process the entire graph in a single pass. Further, out-of-core algorithms are necessary for massive graphs that do not fit in the main memory of a typical system. Researchers can use graph-based solutions for solving problems from many diverse disciplines, including routing and transportation, social networks, bioinformatics, computational science, health care, security and intelligence analysis.

This workshop aims to bring together researchers from different paradigms solving big graph problems under a unified platform for sharing their work and exchanging ideas. We are soliciting novel and original research contributions related to big graph data management, analysis, and mining (algorithms, software systems, applications, best practices, performance). Significant work-in-progress papers are also encouraged. Papers can be from any of the following areas, including but not limited to:

Graph machine learning, graph embeddings, graph neural networks
Representation learning for graph data
Deep Learning-based models for learning on graph data
Extreme-scale computing for large tensor, network, and graph problems
Parallel algorithms for big graph analysis on HPC systems
Heterogeneous CPU-GPU solutions to solve big graph problems
Sampling and summarization of large graphs
Graph algorithms for large-scale scientific computing problems
Graph clustering, partitioning, and classification methods
Scalable graph topology measurement: diameter approximation, eigenvalues, triangle and graphlet counting
Parallel algorithms for computing graph kernels
Inference on large graph data
Graph evolution and dynamic graph models
Graph streams
Computational methods for visualization of large-scale graphs
Graph databases, novel querying and indexing strategies for RDF data
Novel applications of big graph problems in bioinformatics, health care, security, and social networks
New software systems and runtime systems for big graph data mining

Regular paper submissions must be at most 10 pages long, including all figures, tables, and references. They must be formatted according to the paper submission formatting guidelines provided in the IEEE BigData 2019 Call for Papers. Additionally, we encourage short paper submissions (at most 6 pages) describing new work in progress.

Important Dates
Oct 8, 2019 (11.59 pm Anywhere on Earth time): Submission deadline

Nov 1, 2019: Notification of paper acceptance to authors

Nov 15, 2019: Camera-ready submissions due

Dec 9 or Dec 12, 2019: Workshop date



Related Resources

ITAS--EI Compendex, Scopus 2021   2021 Information Technology & Applications Symposium (ITAS 2021)--EI Compendex, Scopus
AVC 2020   Advances in Vision Computing: An International Journal
ICDM 2021   21th Industrial Conference on Data Mining
IEEE ICCCBDA--Scopus and Ei Compendex 2021   2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA 2021)--EI Compendex, Scopus
WSDM 2021   14th ACM Conference on Web Search and Data Mining
PAKDD 2021   Pacific-Asia Conference on Knowledge Discovery and Data Mining
IJU 2020   International Journal of Ubiquitous Computing
HPCA 2021   High-Performance Computer Architecture
HiPC 2020   27th IEEE International Conference on High Performance Computing, Data, and Analytics
ICSC 2021   International Conference on Semantic Computing