#### Third International Workshop on ## High Performance Big Graph Data ## Management, Analysis, and Mining **December 5, 2016** To be held in conjunction with the [2016 IEEE International Conference on Big Data **(IEEE BigData 2016)**](http://cci.drexel.edu/bigdata/bigdata2016/) [Hyatt Regency Washington on Capitol Hill](https://washingtondc.regency.hyatt.com/en/hotel/home.html), Washington, D.C., 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 * 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 Submissions must be at most 8 pages long, including all figures, tables, and references. They must be formatted according to the style files used by the IEEE BigData 2016 conference proceedings. Papers must be submitted online through the [workshop submissions page](http://wi-lab.com/cyberchair/2016/bigdata16/scripts/submit.php?subarea=S19) by 11.59 pm PDT (Pacific Daylight Time) on October 20, 2016. ### Past Workshops [BigGraphs 2014](http://www.biggraphs.org/workshop2014.html) [BigGraphs 2015](http://www.biggraphs.org/workshop2015.html) ### Important Dates * Oct 20, 2016: Submission deadline * Nov 6, 2016: Notification of paper acceptance to authors * Nov 15, 2016: Camera-ready submissions due * Dec 5, 2016: Workshop date ### Keynote **[Nick G. Duffield](https://engineering.tamu.edu/electrical/people/duffield-nick)** ![Nick G. Duffield](duffield.jpg "Nick G. Duffield") Professor, Dept. of Electrical and Computer Engineering Texas A&M University **Adaptive Sampling: From Data Streams to Graph Streams** [slides](Duffield.pdf) ### Workshop Program December 5, 2016 Location: Columbia Foyer meeting room, [Hyatt Regency Washington on Capitol Hill](https://washingtondc.regency.hyatt.com/en/hotel/home.html), Washington, D.C. 15-minute oral presentation of papers (12-minute talk and 3 minutes for Q&A) 8.00 am Opening remarks Mohammad Al Hasan 8.10 am *On the Hyperbolicity of Large-Scale Networks and Its Estimation* *Iraj Saniee*, Sean Kennedy, and Onuttom Narayan 8.25 am *Parallel Graph Mining with Dynamic Load Balancing* Nilothpal Talukder and Mohammed Zaki 8.40 am *Distributed Exact Subgraph Matching in Small Diameter Dynamic Graphs* Charith Wickramaarachchi, Rajgopal Kannan, *Charalampos Chelmis*, and Viktor Prasanna [slides](Chelmis.pdf) 8.55 am Optional 5-minute break 9.00 am Keynote: *Adaptive Sampling: From Data Streams to Graph Streams* Nick G. Duffield [slides](Duffield.pdf) 10.00 am Coffee break 10.20 am *GraphFlow: Workflow-based Big Graph Processing* Sara Riazi and Boyana Norris 10.35 am *Massive Graph Processing on Nanocomputers* *Bryan Rainey* and David Gleich [slides](Rainey.pdf) 10.50 am *GFP-X: A Parallel Approach To Massive Graph Comparison Using Spark* *Stephen Bonner*, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, and Stephen McGough 11.05 am *Deep Topology Classification: A New Approach for Massive Graph Classification* *Stephen Bonner*, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, and Stephen McGough 11.20 am Optional 5-minute break 11.25 am *Fast Reachability Computation on Big Attributed Graphs* *Ka Wai Yung* and Shi-Kuo Chang [slides](Yung.pdf) 11.40 am *Fast distributed k-nn graph update* *Thibault Debatty*, Fabio Pulvirenti, Pietro Michiardi, and Wim Mees [slides](Debatty.pdf) 11.55 am *An Incremental Local-First Community Detection Method for Dynamic Graphs* Hiroki Kanezashi and Toyotaro Suzumura ### 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 [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) Ariful Azad (Lawrence Berkeley National Laboratory) Sanjukta Bhowmick (University of Nebraska at Omaha) Mehmet Deveci (Sandia National Laboratories) Nick Duffield (Texas A&M University) Assefaw Gebremedhin (Washington State University) Oded Green (Georgia Institute of Technology) Irena Holubova (Charles University) Kamesh Madduri (The Pennsylvania State University) Ali Pinar (Sandia National Laboratories) Ryan Rossi (Palo Alto Research Center) George Slota (Rensselaer Polytechnic Institute) Narayanan Sundaram (Intel Labs) Ted Willke (Intel Labs) Yinglong Xia (Huawei Research America) Rong Zhou (Palo Alto Research Center) ### Contact Please send email to one of the workshop organizers.