The Web and Complex Systems (WaCS) Laboratory investigates models and learning algorithms for developing and understanding knowledge from complex systems. Our projects span network and relational data analysis, deep learning, statistical network modeling, and modern data clustering methods. We apply our work in contexts such as web, social, and geospatial informatics. Collaborations with researchers in social computing, data semantics, cybersecurity, information systems and operations research are strongly encouraged.
|Faculty:|| Dr. Derek Doran [derek.doran [at] wright.edu]
|Ning Xie (PhD)|
|Mahdieh Zabihimayvan (PhD)|
|Lakshika Balasuriya (MS)|
|Jace Robinson (MS)|
|Matt Piekenbrock (MS) (see article)|
|Kyle Brown (MS)|
|Matt Maurice (MS)|
|Scott Duberstein||Ethan Wolfer|
|Nathan Rude (MS, 2016)|
|Samir Yelne (MS, 2016)|
|Nripesh Trivedi (BS, 2015)|
Latest News (see the archive for past updates)
7/3/17:Explaining Trained Neural Networks with Semantic Web Technologies: First Steps accepted at the International Workshop on Neural-Symbolic Learning and Reasoning 2017. This paper documents our first (successful) efforts and attaching semantically driven explanations about why a deep network reaches a decision in scene classification tasks.
6/19/17:Seasonality in Dynamic Stochastic Blockmodels accepted at the Workshop on Complex Methods for Data and Web Mining at IEEE/ACM Web Intelligence 2017. This work introduces a new staitsical network model where edge formation probabilities depend on the `type' of each node and on seasonal time series processes that a latent in data. An inference procedure to recover the latent seasonal processes from data is also validated. This is Jace's first publication with WaCS!
6/5/17: A Soft Computing Approach for Benign and Malicious Web Robot Detection accepted in the journal Expert Systems with Applications. The article
describes a new approach to detect Web robot sessions from web logs. By
fuzzy set theory and Markov clustering, the approach selects an appropriate set of classification features, thereby adapting to server-specific session level patterns
automatically. This is Mahdieh's first publication with WaCS!
Current Research Projects
Students: Ning Xie
- M. K. Sarker, N. Xie, D. Doran, M. Raymer, and P. Hitzler. “Explaining Trained Neural Networks with Semantic Web Technologies: First Steps”, Proc. Of 12th Intl. Workshop on Neural-Symbolic Learning and Reasoning, London, United Kingdom, Jul. 2017
This work is supported by the Ohio Federal Research Network.
Students: Kyle Brown, Matt Piekenbrock
The This work is supported by the Oak Ridge Institute for Science and Education and the Air Force Research Laboratories.
Students: Matt Piekenbrock, Jace Robinson
- M. Hashler, M. Piekenbrock, and D. Doran. “dbscan: Fast Density-based Clustering Algorithms in R”, Journal of Statistical Software, 2017
(Status: Accepted Pending Minor Revision)
- J. Robinson and D. Doran. “Seasonality in Dynamic Stochastic Blockmodels”, Proc. of ACM/IEEE Intl. Conference on Web Intelligence, Leipzig, Germany, Aug. 2017
- M. Piekenbrock and D. Doran. "Exploring Information-Optimal Network Discretization for Dynamic Network Analysis", INSNA Sunbelt Conference, Newport Beach, CA, April 2016
- M. Maurice, M. Piekenbrock, and D. Doran. "WAMINet: An Open Source Library for Dynamic Geospace Analysis Using WAMI", Proc. of IEEE Intl. Symposium on Multimedia, Miami, Florida, Dec. 2015
Students: Nathan Rude, Mahdieh Zabihimayvan, Kyle Brown, Ning Xie, Logan Rickert, Scott Duberstein
- M. Zabihimayvan, R. Sadeghi, and D. Doran. "An Integrated Approach for Benign and Malicious Web Robot Detection", Expert Systems With Applications, 2017
- N. Xie, K. Brown, N. Rude, and D. Doran. “A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots”, Proc. Of Intl. Symposium on Neural Networks, Sapporo, Japan, Jun. 2017
- D. Doran and S. Gokhale. "An Integrated Method for Offline and Real-time Web Robot Detection", Expert Systems, 2016
- N. Rude and D. Doran. "Request Type Prediction for Web Robot and Internet of Things Traffic", Proc. of IEEE Intl. Conference on Machine Learning and Applications, Miami, Florida, Dec. 2015
This work is supported by the National Science Foundation under grant #1464104.
Students: Lakshika Balasuriya
- S. Wijeratne, L. Balasuriya, A. Sheth, and D. Doran. “A Semantics-Based Measure of Emoji Similarity”, Proc. Of IEEE/WIC/ACM Intl. Conference on Web Intelligence, Leipzig, Germany, Aug. 2017
- S. Wijeratne, L. Balasuriya, A. Sheth, and D. Doran. “EmojiNet: An Open Service and API for Emoji Sense Discovery”, Proc. Of AAAI Intl. Conference on Weblogs and Social Media, Vancouver, CA, May 2017
- S. Wijeratne, L. Balasuriya, A. Sheth, and D. Doran. “EmojiNet: Building a Machine Readable Sense Inventory for Emoji”, Proc. Of International Conference on Social Informatics, pp. 527-541, Seattle, WA, Nov. 2016
Completed Research Projects
Students: Samir Yelne, Nripesh Trivedi
- N. Trivedi, D. Asamoah, and D. Doran. “Keep the Conversation Going: Engagement-Based Customer Segmentation for Online Social Service Platforms”, Information Systems Frontiers, 2016
- M.C. Calzarossa, L. Massari, D. Doran, S. Yelne, N. Trivedi, and G. Moriarty. “Measuring the Users and Conversations of a Vibrant Online Emotional Support System”, Proc. Of IEEE Symposium on Computers and Communications, pp. 1193-1199, Messina, Italy, Jul. 2016
- D. Doran, S. Yelne, L. Massari, M.C. Calzarossa, L. Jackson, and G. Moriarty. “Stay Awhile and Listen: User Interactions in a Crowdsourced System Offering Emotional Support”, Proc. of IEEE/ACM Intl. Conference on Advances in Social Network Analysis and Mining, pp. 667-674, Paris, France, Aug. 2015
WaCS is very thankful to the following organizations for supporting our research and our student's education: