Diane Staheli’s research while affiliated with Lincoln College USA and other places

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Publications (21)


GraphChallenge.org Triangle Counting Performance
  • Conference Paper

September 2020

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22 Reads

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13 Citations

Siddharth Samsi

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Jeremy Kepner

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Vijay Gadepally

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[...]

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Paul Monticciolo

Fig. 5. Graph Challenge 2018 Finalists. Triangle counting execution time vs number of edges and corresponding model fits for Fox-LLNL-2018 [87], Mailthody-UIUC-2018 [88], and Zhang-CMU-2018 [89].
Fig. 6. Graph Challenge 2018 Innovation Award and Honorable Mentions. Triangle counting execution time vs number of edges and corresponding model fits for Davis-TAMU-2018 [90], Donato-UMassB-2018 [91], and Kuo-CUHK-2018 [92].
Fig. 7. Graph Challenge 2019 Champions and Innovation Awards. Triangle counting execution time vs number of edges and corresponding model fits for Pandey-Stevens-2019 [102], Pearce-LLNL-2019 [103], Acer-Sandia-2019 [104], and Yasar-GaTech-2019 [105].
Fig. 8. Graph Challenge 2019 Student Innovation Award, Finalist, and Honorable Mentions. Triangle counting execution time vs number of edges and corresponding model fits for Hoang-UTexas-2019 [106], Wang-UCDavis-2019 [107], Gui-HuazhongU-2019 [108], and Pearson-UIUC-2019 [109].
GraphChallenge.org Triangle Counting Performance
  • Preprint
  • File available

March 2020

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116 Reads

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, TtriT_{\rm tri}, is a strong function of the number of edges in the graph, NeN_e, which improved significantly from 2017 (Ttri(Ne/108)4/3T_{\rm tri} \approx (N_e/10^8)^{4/3}) to 2018 (TtriNe/109T_{\rm tri} \approx N_e/10^9) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.

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Fig. 1. Descriptive model of cyber sensemaking activities at three levels: data organization and interaction, toolsmithing and analytic interaction, and human-centered assessment.
Considerations for Human-Machine Teaming in Cybersecurity

June 2019

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767 Reads

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3 Citations

Understanding cybersecurity in an environment is uniquely challenging due to highly dynamic and potentially-adversarial activity. At the same time, the stakes are high for performance during these tasks: failures to reason about the environment and make decisions can let attacks go unnoticed or worsen the effects of attacks. Opportunities exist to address these challenges by more tightly integrating computer agents with human operators. In this paper, we consider implications for this integration during three stages that contribute to cyber analysts developing insights and conclusions about their environment: data organization and interaction, toolsmithing and analytic interaction, and human-centered assessment that leads to insights and conclusions. In each area, we discuss current challenges and opportunities for improved human-machine teaming. Finally, we present a roadmap of research goals for advanced human-machine teaming in cybersecurity operations.


Categories of cards within [Network, Mission, Threat] Â [Blue, Grey, Red] paradigm
Study participants by echelon and organization.
Human Centered Cyber Situation Awareness

June 2019

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161 Reads

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2 Citations

Cyber SA is described as the current and predictive knowledge of cyberspace in relation to the Network, Missions and Threats across friendly, neutral and adversary forces. While this model provides a good high-level understanding of Cyber SA, it does not contain actionable information to help inform the development of capabilities to improve SA. In this paper, we present a systematic, human-centered process that uses a card sort methodology to understand and conceptualize Senior Leader Cyber SA requirements. From the data collected, we were able to build a hierarchy of high- and low- priority Cyber SA information, as well as uncover items that represent high levels of disagreement with and across organizations. The findings of this study serve as a first step in developing a better understanding of what Cyber SA means to Senior Leaders, and can inform the development of future capabilities to improve their SA and Mission Performance.




GraphChallenge.org: Raising the Bar on Graph Analytic Performance

May 2018

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76 Reads

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. Graph Challenge 2017 received 22 submissions by 111 authors from 36 organizations. The submissions highlighted graph analytic innovations in hardware, software, algorithms, systems, and visualization. These submissions produced many comparable performance measurements that can be used for assessing the current state of the art of the field. There were numerous submissions that implemented the triangle counting challenge and resulted in over 350 distinct measurements. Analysis of these submissions show that their execution time is a strong function of the number of edges in the graph, NeN_e, and is typically proportional to Ne4/3N_e^{4/3} for large values of NeN_e. Combining the model fits of the submissions presents a picture of the current state of the art of graph analysis, which is typically 10810^8 edges processed per second for graphs with 10810^8 edges. These results are 3030 times faster than serial implementations commonly used by many graph analysts and underscore the importance of making these performance benefits available to the broader community. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.


Keynote: Human-Centric Cyber

October 2017

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27 Reads

In this keynote, I will discuss the importance of human factors in cyber security and highlight lessons learned from conducting user-centered design activities with cyber security analysts. As network traffic volume, interconnectedness of devices, and sophistication of cyber threats all continue to increase, so do concerns about the complexity of providing cyber security. Many research efforts focus on the technology aspects of cyber security; few focus on studying the challenges faced by the human ecosystem of analysts, operators, and senior leaders. User-centered design can help uncover unmet needs and gather requirements to build effective systems to support those that perform cyber security work. Design methods in this domain can help establish user needs, identify opportunities for technology to assist, and evaluate concepts - in this, talk we will discuss examples of each. Ultimately, by embracing the human element of cyber, and positioning the human as the focal point of the research process, we can help the technology community be more efficient at building effective tools. We encourage future cyber security projects to broaden the research methodologies, methods, and techniques at their disposal in order to more completely explore this space. The talk will draw from research experience and field work with users on a number of cyber security research projects. Topics covered will include formative user research and the user-centered design process [1, 2], situation awareness prototyping efforts [3, 4], and evaluation methods for cyber security visualization tools [5].




Citations (15)


... However, our ATC algorithm differs from a typical triangle counting algorithm. In recent years, we have witnessed a surge of new triangle counting algorithms due to the HPEC GraphChallenge [39]. We also observed exciting efforts such as matrix multiplication-based triangle counting [40], new loadbalancing mechanisms [41], [42], and subgraph matchingbased triangle counting [43]. ...

Reference:

Anti-Section Transitive Closure
GraphChallenge.org Triangle Counting Performance
  • Citing Conference Paper
  • September 2020

... There were in total 14 review papers; some of them also presented theoretical or implementation proposals for further development of SA within SOC environments ( Ahmad et al., 2019 ;Andrade and Yoo, 2019 ;Brynielsson et al., 2016 ;Schuster, 2014 , 2016 ;Debatty and Mees, 2019 ;Franke and Brynielsson, 2014 ;Gomez et al., 2019 ;Gutzwiller et al., 2020 ;Hall et al., 2015 ;McNeese and Hall, 2017 ;Nazir and Han, 2022 ;Pahi et al., 2017 ). The reviews ranged in content from specifically addressing the current state of the art of SA within cybersecurity ( Franke and Brynielsson, 2014 ;Gutzwiller et al., 2020 ), to broader reviews concluding with the proposition of a model of cognitive processes within cyber security ( Andrade and Yoo, 2019 ). ...

Considerations for Human-Machine Teaming in Cybersecurity

... Security awareness in cyber security domain can be described as the attainment of current knowledge and prediction of future knowledge about computer networks, missions and threats over neutral, friendly, and adversary forces [1]. The development of situation awareness (SA) has been demonstrated to be helpful in the cyber security domain to specify, analyze, evaluate, and predict human performance in the cyber attack incidents [2], which is especially crucial in real-time changing situations, where decision-making capacity is paramount to solving security tasks. ...

Human Centered Cyber Situation Awareness

... We follow an iterative approach in developing the guideline. In the first phase, two undergraduate students (who are co-authors of this paper) investigated the 2017 VAST Challenge Mini-Challenge 1 [30] following the pre-registration practice of articulating their analysis questions and hypotheses while minimizing their exposure to the raw data. Similar to the process of coding in qualitative studies [16], the two students examined the VAST Challenge independently and met over multiple sessions to achieve internal agreement between their listed analysis questions, as well as their overall analysis approach. ...

VAST Challenge 2017: Mystery at the Wildlife Preserve
  • Citing Conference Paper
  • October 2017

... Due to the widely-recognized importance across various domains, a number of datasets and benchmarks have been developed for network and graph research, including graph data collections [19][20][21], graph learning [22][23][24][25][26][27][28], and diffusion model [29][30][31] (see Section 2 for details). However, none of them provides a comprehensive benchmark suite for flow problems on graphs. ...

GraphChallenge.org: Raising the Bar on Graph Analytic Performance
  • Citing Conference Paper
  • September 2018

... The target audience for these challenges are any individual or team that seeks to highlight their contributions to graph and sparse data analysis software, hardware, algorithms, and/or systems. The Sparse DNN [1]- [9], Stochastic Block Partitioning [10]- [14], Research was sponsored by the Department of the Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Department of the Air Force or the U.S. Government. ...

Streaming graph challenge: Stochastic block partition
  • Citing Conference Paper
  • September 2017

... The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Subgraph Isomorphism [15]- [29], and PageRank [30]- [32] Graph Challenges have enabled a new generation of graph analysis by highlighting the benefits of novel innovations. Graph Challenge is part of the long tradition of challenges that have played a key role in advancing computation, AI, and other fields, such as, YOHO [33], MNIST [34], HPC Challenge [35], ImageNet [36] and VAST [37], [38]. ...

Static graph challenge: Subgraph isomorphism
  • Citing Conference Paper
  • September 2017

... Similarly to Monroe et al. [5], we utilize action classes for creating simpler views. Another example of using action classes can be given based on the IEEE VAST Challenge 2015 story [65], in which visitors of an amusement park attended attractions grouped in 12 classes, such as "thrill rides", "shopping", "food", etc. ...

VAST Challenge 2015: Mayhem at dinofun world