#### Ninth International Workshop on ## High Performance Big Graph Data ## Management, Analysis, and Mining **December 18, 2022** To be held in conjunction with the [2022 IEEE International Conference on Big Data **(IEEE BigData 2022)**](http://bigdataieee.org/BigData2022/) [Osaka International Convention Center](https://bigdataieee.org/BigData2022/hotel.html), Osaka, Japan ### 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 embeddings and representation learning for graph data * Graph neural networks * 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 2022 Call for Papers](http://bigdataieee.org/BigData2022/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) [BigGraphs 2019](workshop2019.html) [BigGraphs 2020](workshop2020.html) [BigGraphs 2021](workshop2021.html) ### Important Dates * Nov 4, 2022 (11.59 pm Anywhere on Earth time): [Submission](https://wi-lab.com/cyberchair/2022/bigdata22/index.php) deadline * Nov 18, 2022: Notification of paper acceptance to authors * Nov 27, 2022: Camera-ready submissions due * Dec 18, 2022: Workshop ### Keynote **[Nesreen Ahmed](http://nesreenahmed.com/)** ![Nesreen Ahmed](ahmed.jpg "Nesreen Ahmed") Senior Staff Research Scientist Intel Research Labs **Graph Learning and Systems** ### Workshop Program December 18, 2022 10:30 am -- 1:30 pm Japanese Standard Time Location: Room 1006, [Osaka International Convention Center](https://bigdataieee.org/BigData2022/hotel.html), Osaka, Japan 20-minute oral presentation of papers (15-minute talk and 5 minutes for Q&A) 10:30 am   Opening Remarks Mohammad Al Hasan Data Fusion and Graph Analysis in Fraud Transaction Detection: walkthrough of a case study Valerio Bellandi and Stefano Siccardi 10:35 am   Pipelining Graph Construction and Agent-based Computation over Distributed Memory Yan Hong and Munehiro Fukuda 10:55 am   Functional Ball Dropping: A superfast hypergraph generation scheme Lilith Hafner, Chase Holdener, and Nicole Eikmeier 11:20 am   BigGraphs 2022 Keynote: Graph Learning and Systems Nesreen Ahmed 12:15 pm   CrunchQA - A Synthetic Dataset for Question Answering over Crunchbase Knowledge Graph Lifan Yu, Nadya Abdel Madjid, and Djellel Difallah 12:40 pm   Node Context Selection in Transformer-Based Graph Representation Learning Models Tianze Wang, Amir H. Payberah, and Vladimir Vlassov 1:00 pm   Scalable Forecasting in Sensor Networks with Graph Convolutional LSTM Models Massimiliano Altieri, Roberto Corizzo, and Michelangelo Ceci 1:25 pm   Closing remarks Mohammad Al Hasan ### 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://arifuzzaman.faculty.unlv.edu/) ![Shaikh Arifuzzaman](arif.jpg "Shaikh Arifuzzaman") Department of Computer Science University of Nevada, Las Vegas Las Vegas, NV 89154 [Kamesh Madduri](https://madduri.org/) ![Kamesh Madduri](madduri.jpg "Kamesh Madduri") Department of Computer Science and Engineering The Pennsylvania State University University Park, PA 16802 ### Contact Please send email to one of the workshop organizers.