#### Sixth International Workshop on
## High Performance Big Graph Data
## Management, Analysis, and Mining
**December 12, 2019**
To be held in conjunction with the
[2019 IEEE International Conference on Big Data **(IEEE BigData 2019)**](http://bigdataieee.org/BigData2019/)
[The Westin Bonaventure Hotel & Suites](http://bigdataieee.org/BigData2019/Hotel.html), 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](http://bigdataieee.org/BigData2019/CallPapers.html). Additionally, we encourage **short paper** submissions (at most 6 pages) describing new work in progress.
### Past Workshops
[BigGraphs 2014](workshop2014.html)
[BigGraphs 2015](workshop2015.html)
[BigGraphs 2016](workshop2016.html)
[BigGraphs 2017](workshop2017.html)
[BigGraphs 2018](workshop2018.html)
### Important Dates
* Oct 8, 2019 (11.59 pm Anywhere on Earth time): [Submission](https://wi-lab.com/cyberchair/2019/bigdata19/scripts/submit.php?subarea=S28&undisplay_detail=1&wh=/cyberchair/2019/bigdata19/scripts/ws_submit.php) deadline
* Nov 4, 2019: Notification of paper acceptance to authors
* Nov 20, 2019: Camera-ready submissions due
* Dec 12, 2019 (8 am -- 12:30 pm): Workshop
### Accepted Papers
Esra Akbas and Mehmet Aktas
Network Embedding: on Compression and Learning
Md Abdul Motaleb Faysal and Shaikh Arifuzzaman
Distributed Community Detection in Large Networks using An Information-Theoretic Approach
M. Shamimul Hasan, Drew Schmidt, Ramakrishnan Kannan, and Neena Imam
A Scalable Graph Analytics Framework for Programming with Big Data in R (pbdR)
Hubert Naacke, Ke Li, Bernd Amann, and Olivier CurĂ©
Efficient similarity-based alignment of temporally-situated graph nodes with Apache Spark
Vijay Walunj, Gharib Gharibi, Duy Ho, and Yugyung Lee
GraphEvo: Characterizing and Understanding Software Evolution using Call Graphs
Xiantian Zhou and Carlos Ordonez
Computing Complex Graph Properties with SQL Queries
### Workshop Organizers
[Nesreen Ahmed](http://nesreenahmed.com/)
![Nesreen Ahmed](ahmed.jpg "Nesreen Ahmed")
Intel Labs
Santa Clara, CA 95054
[Mohammad Al Hasan](http://cs.iupui.edu/~alhasan/)
![Mohammad Al Hasan](hasan.jpg "Mohammad Al Hasan")
Department of Computer and Information Science
Indiana University - Purdue University
Indianapolis, IN 46202
[Shaikh Arifuzzaman](https://www.cs.uno.edu/~arif/)
![Shaikh Arifuzzaman](arif.jpg "Shaikh Arifuzzaman")
Department of Computer Science
The University of New Orleans
New Orleans, LA 70148
[Kamesh Madduri](http://www.cse.psu.edu/~kxm85/)
![Kamesh Madduri](madduri.jpg "Kamesh Madduri")
Department of Computer Science and Engineering
The Pennsylvania State University
University Park, PA 16802
### Program Committee
Nesreen Ahmed, Intel Labs
Mohammad Al Hasan, Indiana University - Purdue University
Shad Kirmani, eBay
John Boaz Lee, Worcester Polytechnic Institute
Kamesh Madduri, Pennsylvania State University
Ryan Rossi, Adobe Research
Hongyuan Zhan, Facebook
Rong Zhou, Google
### Contact
Please send email to one of the workshop organizers.