#### Seventh International Workshop on ## High Performance Big Graph Data ## Management, Analysis, and Mining **December 12, 2020** To be held in conjunction with the [2020 IEEE International Conference on Big Data **(IEEE BigData 2020)**](http://bigdataieee.org/BigData2020/) ### 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 2020 Call for Papers](http://bigdataieee.org/BigData2020/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) ### Important Dates * Oct 8, 2020 (11.59 pm Anywhere on Earth time): [Submission](https://wi-lab.com/cyberchair/2020/bigdata20/index.php) deadline * Nov 5, 2020: Notification of paper acceptance to authors * Nov 15, 2020: Camera-ready submissions due * Dec 12, 2020: Workshop to be held virtually ### Keynote **[Madhav Marathe](https://engineering.virginia.edu/faculty/madhav-marathe)**  University of Virginia ### Workshop Program December 12, 2020 9:00 am -- 2:30 pm U.S. Eastern Standard Time Location: Virtual, to be announced by conference organizers. 25-minute presentations (20-minute talk video will be broadcast and 5 minutes for live Q&A) 9:00 am     Opening remarks Workshop organizers 9:05 am     Approximately and Efficiently Estimating Dynamic Point-to-Point Shortest Path Alok Tripathy and Oded Green 9:30 am     Agent-Navigable Dynamic Graph Construction and Visualization over Distributed Memory Justin Gilroy, Satine Paronyan, Jonathan Acoltzi, and Munehiro Fukuda 9:55 am     Graph Filtering to Remove the "Middle Ground" for Anomaly Detection William Eberle and Lawrence Holder 10:25 am     Keynote by Prof. Madhav Marathe 11:20 am     Massively Parallel Processing Database for Sequence and Graph Data Structures Applied to Rapid-Response Drug Repurposing Chris Rickett, Kristyn Maschoff, and Sreenivas Sukumar 11:45 am     Retrieving Entities from Knowledge Graphs without Knowing Much: On Learning Generalizable Patterns and Indexing Yuting Xie and Tingjian Ge 12:10 pm     Graph Force Learning Ke Sun, Jiaying Liu, Shuo Yu, Bo Xu, and Feng Xia 12:35 pm     Memory Efficient Graph Convolutional Network based Distributed Link Prediction Damitha Senevirathne, Isuru Wijesiri, Suchitha Dehigaspitiya, Miyuru Dayarathna, Sanath Jayasena, and Toyotaro Suzumura 1:00 pm     Break 1:10 pm     Understanding Coarsening for Embedding Large-Scale Graphs Taha Atahan Akyıldız, Amro Alabsi Alundi, and Kamer Kaya 1:35 pm     Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks Kexuan Sun, Shudan Zhong, and Hong Xu 2:00 pm     Relational Graph Embeddings for Table Retrieval Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, and Jeff Heflin 2:25 pm     Closing remarks Workshop organizers ### Workshop Organizers [Nesreen Ahmed](http://nesreenahmed.com/)  Intel Labs Santa Clara, CA 95054 [Mohammad Al Hasan](http://cs.iupui.edu/~alhasan/)  Department of Computer and Information Science Indiana University - Purdue University Indianapolis, IN 46202 [Shaikh Arifuzzaman](https://www.cs.uno.edu/~arif/)  Department of Computer Science The University of New Orleans New Orleans, LA 70148 [Kamesh Madduri](https://madduri.org/)  Department of Computer Science and Engineering The Pennsylvania State University University Park, PA 16802 ### Contact Please send email to one of the workshop organizers.