posted by organizer: kmadduri || 302 views || tracked by 3 users: [display]

BigGraphs 2018 : International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs)

FacebookTwitterLinkedInGoogle

Link: http://www.biggraphs.org/
 
When Dec 10, 2018 - Dec 10, 2018
Where Seattle, WA
Submission Deadline Oct 19, 2018
Notification Due Nov 7, 2018
Final Version Due Nov 18, 2018
Categories    computer science   big data   data mining   high performance computing
 

Call For Papers

BigGraphs 2018
----
Fifth International Workshop on
High Performance Big Graph Data
Management, Analysis, and Mining

biggraphs.org

December 10, 2018

To be held in conjunction with the
2018 IEEE International Conference on Big Data (IEEE BigData 2018)
The Westin Seattle, Seattle, WA, 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:

* Parallel algorithms for big graph analysis on HPC systems
* Heterogeneous CPU-GPU solutions to solve big graph problems
* Extreme-scale computing for large graph, tensor, and network 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
* Representation Learning for graph data
* Computational methods for visualization of large-scale graphs
* Deep Learning based models for learning on graph data
* 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 style files used by the IEEE BigData 2018 conference proceedings. Additionally, we encourage short paper submissions (at most 6 pages) describing new work in progress.


Important Dates
----
* Oct 19, 2018 (11.59 pm Pacific time): Submission deadline
* Nov 7, 2018: Notification of paper acceptance to authors
* Nov 18, 2018: Camera-ready submissions due
* Dec 10, 2018: Workshop date


Organizers
----
Nesreen Ahmed, Intel Labs
Mohammad Al Hasan, Indiana University - Purdue University
Shaikh Arifuzzaman, University of New Orleans
Kamesh Madduri, Pennsylvania State University

Related Resources

ICDM 2019   19th Industrial Conference on Data Mining ICDM 2019
ParCo 2019   Parallel Computing Conference
MLDM 2019   15th International Conference on Machine Learning and Data Mining MLDM 2019
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning
BDE--EI Compendex, Scopus 2019   2019 International Conference on Big Data Engineering (BDE 2019)--EI Compendex, Scopus
BDAI--Ei and Scopus 2019   2019 2nd International Conference on Big Data and Artificial Intelligence (BDAI 2019)--Ei Compendex and Scopus
HPC 2019   High Performance Computing
ISCSAI 2018   2018 International Symposium on Computer Science and Artificial Intelligence
ACIIDS 2018   10th Asian Conference on Intelligent Information and Database Systems
ISBDAI 2018   2018 International Symposium on Big Data and Artificial Intelligence