SIAM News Blog

Data Mining "Brings It" to SIAM Annual Meeting

By Jeremy KepnerSanjukta BhowmickAydın BuluçRajmonda CaceresJordan CrouserVijay GadepallyBen Miller, and Jennifer Webster

The 2017 International Conference on Data Mining (SDM17), to be held from April 27-29 in Houston, Texas, is just around the corner! In honor of the upcoming meeting, we look back at some of the data mining highlights from minisymposia at last year's Annual Meeting, courtesy of the Activity Group on Data Mining and Analytics. Be sure to register for SDM17 ASAP  pre-registration ends on March 30th!

The Activity Group on Data Mining and Analytics (SIAG/DMA) is one of SIAM's most vibrant and dynamic activity groups. To better share its enthusiasm for data mining with the broader SIAM community, SIAG/DMA organized six minisymposia at the 2016 Annual Meeting. These sessions included 48 talks organized by 11 SIAM members on the following topics:

  • GraphBLAS (Aydın Buluç)
  • Algorithms and Statistical Methods for Noisy Network Analysis (Sanjukta Bhowmick & Ben Miller)
  • Inferring Networks from Non-network Data (Rajmonda Caceres, Ivan Brugere, and Tanya Y. Berger-Wolf)
  • Visual Analytics (Jordan Crouser)
  • Mining in Graph Data (Jennifer Webster, Mahantesh Halappanavar, and Emilie Hogan)
  • Scientific Computing and Big Data (Vijay Gadepally)

The minisymposia were well received by the broader SIAM community; below are some of the key highlights.


The theory of using matrices and vectors for graph computations has a long history; Graph Algorithms in the Language of Linear Algebra, a SIAM book by Jeremy Kepner and John Gilbert [1], offers a snapshot of the state of the art. High-performance graph algorithms are often implemented with sparse matrices and linear algebra in many graph-processing systems. Example systems include the Combinatorial BLAS [2], D4M [3], GraphMat [4], and GPI [5]. [6] is a community initiative that standardizes these different efforts to build a common foundation for graph algorithm developers. The GraphBLAS minisymposium had eight talks. Aydın Buluç from Lawrence Berkeley National Laboratory discussed the current status of the C language API and ongoing efforts to develop a GraphBLAS-compliant parallel library in partitioned global address space (PGAS) languages. Jose Moreira and Manoj Kumar from IBM presented the Graph Programming Interface (GPI), as well as a proposal for a common binary format for graph storage. Carl Yang from the University of California, Davis talked about the implementation of breadth-first search utilizing the GraphBLAS primitives on clusters of GPU-equipped computers. 

Andrew Lumsdaine from Indiana University spoke about software and systems issues related to the implementation of the GraphBLAS Template Library (GBTL) [7] on different backends, such as CPUs and GPUs. Jeremy Kepner from the Massachusetts Institute of Technology (MIT)’s Lincoln Laboratory introduced the mathematical foundations of GraphBLAS [8], with an emphasis on incidence matrices as a preferred representation for graphs in databases. Scott McMillan from the Carnegie Mellon University Software Engineering Institute dived deeper into the details of the GBTL library, with a focus on its frontend design. Narayanan Sundaram from Intel presented GraphMat (and its distributed cousin GraphPad), a highly-optimized graph library whose frontend is based on vertex programming and whose backend is based on linear algebra operations. Finally, Michael Wolf from Sandia National Laboratories discussed miniTri [9], a triangle enumeration-based data analytics miniapp, with specific focus on a linear algebraic algorithm. Approximately 25 people attended the sessions, and engaged in lively discussions during breaks. 

Algorithms and Statistical Methods for Noisy Network Analysis

Handing errors and noise is a common problem that the network science research community is beginning to tackle. A two-part minisymposium demonstrated the diversity of approaches to this problem, focusing on statistical methods and algorithms for addressing issues arising from noise in networks. Several presentations centered on useful network properties, such as centrality metrics and connected components, and the ways noise in observations can affect analysis [10]. These talks included generative models for networks and statistically rigorous methods to estimate properties from sampled data [11]. Other talks concentrated on filtering techniques, such as the use of metadata to narrow a search from a cue vertex or emphasis of an interesting substructure [12]. Some speakers discussed how noise affects the analysis in specific disciplines, including collaboration science [13], bioinformatics [14], and cybersecurity. The minisymposium concluded with a conversation between the speakers and the audience on the sessions’ common themes. Participants agreed that, as noisy network analysis continues to evolve as a subfield, addressing the lack of a common framework for modeling and quantifying noise is an exceptionally important challenge that would allow synthesis of related research in many diverse areas.

Inferring Networks from Non-Network Data

This minisymposium explored the important topic of network representation learning. In many practical settings, researchers must make arbitrary decisions regarding how to construct networks from noisy, indirect, and diverse data. Presentations on both sessions covered important highlights from the current state of the art for this emerging research area. Several speakers spoke about the importance of connecting the objective of a learning task—be it a link prediction, diffusion estimation, or vertex classification—to the process of constructing and evaluating network representations [15-19]. Another important theme emphasized domain-specific notions of quality – in the context of constructing robust correlation networks from biological and climate data, for example [20, 21]. Overall, the minisymposium helped consolidate important ideas, insights, and perspectives aimed at developing a rigorous and cohesive framework for learning robust network representations.

Scientific Computing and Big Data

This two-part minisymposium was a great success. It featured nine speakers from diverse organizations that shared their considerable experience working with scientific big data. The first session included talks from Vijay Gadepally (MIT), Siddharth Samsi (MIT), Manoj Kumar (IBM Research), Michel Kinsy (Boston University), and  Shashank Yellapantula (GE Global Research). Gadepally and Samsi discussed advances in data management technologies [22–25], while Kumar presented a brief overview of a graph-based API that IBM is developing [26]. Kinsy introduced a novel processing architecture for low power computations [27], and Yellapantula discussed GE's big data problems and the many potential areas of collaboration within the wider SIAM community [28]. 

The second session featured a number of speakers from the medical community. Ashok Krishnamurthy (Renaissance Computing Institute, the University of North Carolina (UNC), Chapel Hill) presented the UNC Health Center’s development of a large-scale clinical data warehouse [29]. Steve Finkbeiner (the University of California, San Francisco, Gladstone Institute) talked about his group’s development of new robotic sensors capable of generating terabytes of imaging data per day to better understand the affects and causes of amyotrophic lateral sclerosis (ALS) [30]. Andy Zimolzak (Harvard University, Department of Veteran Affairs) discussed his group’s work in developing computational infrastructure for precision oncology [31]. Aaron Elmore (University of Chicago) concluded the second session by presenting a new tool his research team is developing as the GitHub for data – DataHub [32]. The talks were of great interest to the diverse audience, with many interesting discussions during the two sessions. Overall, the speakers and participants obtained a greater understanding of some domain-specific problems and technical strategies for addressing such problems.

Mining in Graph Data

This minisymposium began with organizer Jennifer Webster presenting an overview of the topic. Webster covered some common issues, including the use of potentially-messy found data and the bias stemming from the translation of real-world problems into the language of mathematics. She highlighted these issues with examples drawn from shipping networks. Following that presentation, organizer Mahantesh Halappanavar spoke about algorithms for large-scale community detection. In particular, he described his parallel implementation of the Louvain modularity maximization method. The convergence results showed close agreement with the serial implementation, but the speed-up on multiple processors was significant. Halappanavar’s group tested graphs with up to 50 million vertices and 2 billion edges, and conducted the work jointly with Ananth Kalyanaraman. 

Jevin West discussed mining information from citation networks. He presented "the map equation," which is based on dynamics of movement in a network and used to discover communities from those dynamics. He offered a demo of his software, conveying how this method can help discover the time evolution of communities. Kamesh Madduri, the final speaker of the morning session, discussed a matrix factorization method for evaluating network community structure. When given a graph and a set of communities, he uses a non-negative matrix factorization to discover the relative importance of communities. An advantage of this work is its ability to accommodate overlapping communities. These four talks rounded out the morning session, with steady attendance of around 40 listeners for all talks.

The minisymposium continued in the afternoon with David Haglin discussing (in his words, “ranting about”) the many situations in which hyper-multi-graphs can be used and the current algorithmic and computational resource limitations to the analysis of such graphs. Haglin offered several examples of graphs in cyber and social networks, especially those where non-numeric edge information arises and the created graphs become extremely large. Sanjukta Bhowmick then presented her metrics for community permanence that aid in the mitigation of noise present in real-world graphs. The permanence metric performed well across a variety of benchmark graphs and real-world data sets, and showed the stability of communities. Robert Bridges then demonstrated the use of graph analysis techniques to locate both anomalous cyber activity and the more friendly changes in American football conferences.  Bridges’ methods dealt with time-varying graphs, noisy data, and a host of other challenges in the generation, creation, and analysis of these graphs. In the final talk of the minisymposium Ariful Azad compared communities across graphs. When given two related graphs with communities identified, one might ask how the communities compare across those graphs. Azad offered examples of graphs created from biological data, such as MRI scans, and image segmentation over time. He used the Mixed Edge Cover (MEC) algorithm to match corresponding communities, and provided experimental results in the example data sets to exhibit algorithm performance.

Visual Analytics

R. Jordan Crouser of Smith College organized the Visual Analytics minisymposium. Visual analytics is “the science of analytical reasoning facilitated by interactive visual interfaces” [33], and is rapidly gaining ground as an important discipline complementary to applied mathematics. The two-session series featured speakers from Smith College, Worchester Polytechnic Institute, Bucknell University, DePaul University, Washington University, MIT’s Lincoln Laboratory, and IBM Research, and covered topics ranging from the design and evaluation of visual analytics systems to the role of human perception in data analysis.

Acknowledgments: This material is based in part upon work supported by the NSF under grant number DMS-1312831, by DOE ASCR under contract number DE-AC02-05CH11231, and by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the United States Government.

[1] Kepner, J., & Gilbert, J. (Eds.). (2011). Graph algorithms in the language of linear algebra. Society for Industrial and Applied Mathematics.
[2] Buluç, A., & Gilbert, J. R. (2011). The Combinatorial BLAS: Design, implementation, and applications. The International Journal of High Performance Computing Applications, 25(4), 496-509.
[3] Kepner, J., Arcand, W., Bergeron, W., Bliss, N., Bond, R., Byun, C., ... & McCabe, A. (2012, March). Dynamic distributed dimensional data model (D4M) database and computation system. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference (pp. 5349-5352). IEEE.
[4] Sundaram, N., Satish, N., Patwary, M.M.A., Dulloor, S.R., Anderson, M.J., Vadlamudi, S.G., Das, D. and Dubey, P., (2015). GraphMat: High performance graph analytics made productive. Proceedings of the VLDB Endowment, 8(11), 1214-1225.
[5] Ekanadham, K., Horn, W. P., Kumar, M., Jann, J., Moreira, J., Pattnaik, P., Serrano, M., Tanase, G. & Yu, H. (2016, May). Graph programming interface (GPI): a linear algebra programming model for large scale graph computations. In Proceedings of the ACM International Conference on Computing Frontiers (pp. 72-81). ACM.
[6] Mattson, T., Bader, D., Berry, J., Buluc, A., Dongarra, J., Faloutsos, C., Feo, J., Gilbert, J., Gonzalez, J., Hendrickson, B. and Kepner, J., (2013, September). Standards for graph algorithm primitives. In High Performance Extreme Computing Conference (HPEC), 2013 IEEE (pp. 1-2). IEEE.
[7] Zhang, P., Zalewski, M., Lumsdaine, A., Misurda, S., & McMillan, S. (2016, May). GBTL-CUDA: Graph Algorithms and Primitives for GPUs. In Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International (pp. 912-920). IEEE.
[8] Kepner, J., Aaltonen, P., Bader, D., Buluç, A., Franchetti, F., Gilbert, J., Hutchison, D., Kumar, M., Lumsdaine, A., Meyerhenke, H. and McMillan, S., (2016, September). Mathematical foundations of the GraphBLAS. In High Performance Extreme Computing Conference (HPEC), 2016 IEEE (pp. 1-9). IEEE.
[9] Wolf, M. M., Edwards, H. C., & Olivier, S. L. (2016, September). Kokkos/Qthreads task-parallel approach to linear algebra based graph analytics. In High Performance Extreme Computing Conference (HPEC), 2016 IEEE (pp. 1-7). IEEE.
[10] Segarra, S. and Ribeiro, A. (2016). Stability and continuity of centrality measures in weighted graphs. IEEE Transactions on Signal Processing, 64(3), 543-555. IEEE.
[11] Ganguly, A. and Kolaczyk, E. (2017). Estimation of vertex degrees in a sampled network. Preprint, arXiv:1701.7203.
[12] Smith, S., Caceres, R., Senne, K., McMahon, M., and Greer, T. (2017). Network Discovery Using Content and Homophily. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on (to appear). IEEE.
[13] Bliss, N.T., Peirson, B.R.E., Painter, D., and Laubichler, M. D. (2014). Anomalous subgraph detection in publication networks: Leveraging truth. In Signals, Systems and Computers, 2014 48th Asilomar conference on (pp. 2005–2009). IEEE.
[14] Dempsey, K., Chen, T.-Y., Srinivasan, S., Bhowmick, S., Hesham, A. (2013). A structure-preserving hybrid-chordal filter for sampling in correlation networks. In High Performance Computing and Simulation (HPCS), 2013 International Conference on (pp. 243–250).
[15] Gleich, D. F., Mahoney, M.W (2015). Using local spectral methods to robustify graph-based learning algorithms. In Proc. of the 21th ACM SIGKDD Inter. Conf. on Knowledge Discovery and Data Mining (p 359-368).
[16] Fish. B, Caceres, R.S. (2015). Handling oversampling in dynamic networks using link prediction. In Joint European Conf. on Machine Learning and Knowledge Discovery in Databases (ECML PKDD) (p 671-686).
[17] D'Amour, A., Airoldi, E. Misspecification. (2016). Sparsity, and Superpopulation Inference for Sparse Social Networks.   
[18] De, A., Valera, I., Ganguly, N., Bhattacharya, S.,Rodriguez, M. G. (2016). Learning and Forecasting Opinion Dynamics in Social Networks, Advances in Neural Information Processing Systems.
[19] Brugere, I. (2015). Latent Graph Inference and Validation, IEEE Inter. Conf. on Data Mining Workshop (ICDMW).
[20] Agrawal, S., Atluri, G., Liess, S., Chatterjee, S.,Kumar, V. Tripoles (2015). A New Class of Climate Teleconnections, Technical Report.
[21] Marbach, D., Lamparter D., Quon, G., Kellis M., Kutalik, Z., Bergmann, S. (2016). Tissue-Specific Regulatory Circuits Reveal Variable Modular Perturbations Across Complex Diseases. Nature Methods, 13, p 366-370.
[22] Elmore, A., Duggan, J., Stonebraker, M., Balazinska, M., Cetintemel, U., Gadepally, V., ... & Madden, S. (2015). A demonstration of the bigdawg polystore system. Proceedings of the VLDB Endowment, 8(12), 1908-1911.
[23] Gadepally, V., Chen, P., Duggan, J., Elmore, A., Haynes, B., Kepner, J., ... & Stonebraker, M. (2016, September). The BigDAWG polystore system and architecture. In High Performance Extreme Computing Conference (HPEC), 2016 IEEE (pp. 1-6). IEEE.
[24] Samsi, S., Brattain, L., Arcand, W., Bestor, D., Bergeron, B., Byun, C., ... & Michaleas, P. (2016, September). Benchmarking SciDB data import on HPC systems. In High Performance Extreme Computing Conference (HPEC), 2016 IEEE (pp. 1-5). IEEE.
[25] Hutchison, D., Kepner, J., Gadepally, V., & Fuchs, A. (2015, September). Graphulo implementation of server-side sparse matrix multiply in the Accumulo database. In High Performance Extreme Computing Conference (HPEC), 2015 IEEE (pp. 1-7). IEEE.
[26] Horn, W., Kumar, M., Jann, J., Moreira, J., Pattnaik, P., Serrano, M., ... & Yu, H. (2017). Graph Programming Interface (GPI): A Linear Algebra Programming Model for Large Scale Graph Computations. International Journal of Parallel Programming, 1-29.
[27] Kinsy, M. A., Pellauer, M., & Devadas, S. (2011, September). Heracles: Fully synthesizable parameterized mips-based multicore system. In Field Programmable Logic and Applications (FPL), 2011 International Conference on (pp. 356-362). IEEE.
[28] Yellapantula, S., Venkatesan, K., Pratt, A., Slabaugh, C., & Lucht, R. P. (2016). LES validation practices in a model aero-engine combustor at engine relevant conditions. In 52nd AIAA/SAE/ASEE Joint Propulsion Conference (p. 4785).
[29] Evans, J. P., Wilhelmsen, K. C., Berg, J., Schmitt, C. P., Krishnamurthy, A., Fecho, K., & Ahalt, S. C. (2016). A New Framework and Prototype Solution for Clinical Decision Support and Research in Genomics and Other Data-intensive Fields of Medicine. eGEMs, 4(1).
[30] Lammel, G., Armbruster, S., Schelling, C., Benzel, H., Brasas, J., Illing, M., ... & Finkbeiner, S. (2005, June). Next generation pressure sensors in surface micromachining technology. In Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS'05. The 13th International Conference on (Vol. 1, pp. 35-36). IEEE.
[31] Celi, L. A., Zimolzak, A. J., & Stone, D. J. (2014). Dynamic clinical data mining: search engine-based decision support. JMIR medical informatics, 2(1), e13.
[32] Bhardwaj, A., Bhattacherjee, S., Chavan, A., Deshpande, A., Elmore, A. J., Madden, S., & Parameswaran, A. G. (2014). Datahub: Collaborative data science & dataset version management at scale. Preprint, arXiv:1409.0798.
[33] Thomas, James J. and Cook, Kristin, eds. Illuminating the path:[the research and development agenda for visual analytics]. IEEE Computer Society, 2005.

Jeremy Kepner is chair of the SIAM Activity Group on Data Mining and Analytics (SIAG/DMA) and a fellow at the Massachusetts Institute of Technology's Lincoln Laboratory, where he leads the Supercomputing Center. Sanjukta Bhowmick is an associate professor of computer science in the College of Information Science and Technology at the University of Nebraska Omaha. Aydın Buluç is a staff scientist at the Lawrence Berkeley National Laboratory. Rajmonda S. Caceres is part of the Technical Staff in the Cyber Analytics and Decision Systems Group at MIT's Lincoln Laboratory. Jordan Crouser is a visiting assistant professor in the Statistical and Data Sciences Program at Smith College. Vijay Gadepally is a researcher at the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (Member of the Technical Staff) and Computer Science and Artificial Intelligence Laboratory (CSAIL). Benjamin A. Miller is part of the Technical Staff in the Cyber Analytics and Decision Systems Group at MIT Lincoln Laboratory. Jennifer Webster is a mathematician in the Applied Statistics and Computational Modeling group at Pacific Northwest National Laboratory.

blog comments powered by Disqus