These are some of my major research areas:

  • Network sampling/crawling: Before we can analyze a network, we need to collect data. But this can be time-consuming and expensive. How can we collect data in a way that gives us the most bang for our buck?
    • Katchaguy Areekijseree and Sucheta Soundarajan. “Measuring the Sampling Robustness of Complex Networks.” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2019. (Forthcoming.)
    • Katchaguy Areekijseree and Sucheta Soundarajan. “Crawling Complex Networks: An Experimental Evaluation of Data Collection Algorithms and Network Structural Properties.” Journal of Web Science (JWS). 2019. (Forthcoming.)
    • Ricky Laishram, Jeremy D. Wendt, and Sucheta Soundarajan. “Crawling the Community Structure of Multiplex Networks.” AAAI Conference on Artificial Intelligence (AAAI). 2019.
    • Katchaguy Areekijseree and Sucheta Soundarajan. “DE-Crawler: A Densification-Expansion Algorithm for Online Data Collection.” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2018. PDF
    • Humphrey Mensah and Sucheta Soundarajan. “Sampling Community Structure in Dynamic Social Networks.” International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. 2018. PDF
    • Katchaguy Areekijseree, Ricky Laishram, and Sucheta Soundarajan. “Guidelines for Online Network Crawling: A Study of Data Collection Approaches and Network Properties.” ACM Web Science Conference (WebSci). 2018. PDF
    • Pivithuru Wijegunawardana, Vatsal Ojha, Ralucca Gera, and Sucheta Soundarajan. “Sampling Dark Networks to Locate People of Interest.” Social Network Analysis and Mining (SNAM). 2018. PDF
    • Sucheta Soundarajan, Tina Eliassi-Rad, Brian Gallagher, and Ali Pinar. epsilon-WGX: Adaptive Edge Probing for Enhancing Incomplete Networks. ACM Web Science Conference (WebSci). 2017. PDF
    • Ricky Laishram, Katchaguy Areekijseree, and Sucheta Soundarajan. Predicted Max Degree Sampling: Sampling in Directed Networks to Maximize Node Coverage through Crawling. INFOCOM Workshop on Network Science for Communication Network (NetSciCom). 2017. PDF
    • Pivithuru Wijegunawardana, Vatsal Ojha, Ralucca Gera and Sucheta Soundarajan. Seeing Red: Locating People of Interest in Networks. Conference on Complex Networks (CompleNet). 2017. PDF
    • Jeremy D. Wendt, Randy Wells, Richard V. Field Jr., and Sucheta Soundarajan. On data collection, graph construction, and sampling in Twitter. International Symposium on Foundations and Applications of Big Data Analytics (FAB). 2016. PDF.
    • Sucheta Soundarajan, Tina Eliassi-Rad, Brian Gallagher, Ali Pinar. MaxReach: Reducing Network Incompleteness through Node Probes. IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM). 2016. PDF.
    • Sucheta Soundarajan, Acar Tamersoy, Elias B Khalil, Tina Eliassi-Rad, Duen Horng Chau, Brian Gallagher, and Kevin Roundy. Generating Graph Snapshots from Streaming Edge Data. International Conference on the World Wide Web (WWW). 2016.PDF
  • Adversarial social network analysis: When dealing with so-called "dark" networks, containing terrorists, criminals, or other covert groups, the available network data may contain errors intended to mislead analysts. How can we collect and analyze network data under such circumstances? This work is funded by Army Research Office grant #72265-NS-YIP
    • Katchaguy Areekijseree and Sucheta Soundarajan. “Measuring the Sampling Robustness of Complex Networks.” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2019. (Forthcoming.)
    • Ricky Laishram, Jeremy D. Wendt, and Sucheta Soundarajan. “Crawling the Community Structure of Multiplex Networks.” AAAI Conference on Artificial Intelligence (AAAI). 2019.
    • Ricky Laishram, Ahmet Erdem Sariyuce, Tina Eliassi-Rad, Ali Pinar, and Sucheta Soundarajan. “Measuring and Improving the Core Resilience of Networks.” The Web Conference (WWW). 2018. PDF
    • Pivithuru Wijegunawardana, Vatsal Ojha, Ralucca Gera, and Sucheta Soundarajan. “Sampling Dark Networks to Locate People of Interest.” Social Network Analysis and Mining (SNAM). 2018. PDF
    • Pivithuru Wijegunawardana, Vatsal Ojha, Ralucca Gera and Sucheta Soundarajan. Seeing Red: Locating People of Interest in Networks. Conference on Complex Networks (CompleNet). 2017. PDF
  • Designing fair algorithms: Algorithms are used extensively in many different parts of society, but it is becoming clear that they might sometimes have negative effects, such as unethically discriminating against so-called 'protected' groups of people (those who share a common trait like race or gender). How can we detect such discrimination, and how can we avoid it? Potential solutions include removing the effect of a protected attribute on network structure and modifying the output of an algorithm to 'debias' it.
    • Sucheta Soundarajan and Daniel Clausen. “Equal Protection Under the Algorithm: A Legal-Inspired Framework for Identifying Discrimination in Machine Learning.” Fairness, Accountability, and Transparency in Machine Learning (FAT/ML). 2018. PDF
    • Kun He, Yingru Li, Sucheta Soundarajan, and John Hopcroft. “Hidden Community Detection in Social Networks.” Information Sciences. 2018. PDF
  • Using memristors for efficient spectral decomposition: The memristor is a newly created hardware component. It is like a resistor, but the resistance can be changed in real time. When wired together into a crossbar array, memristors can be used to perform extremely fast matrix-vector multiplication and solving of systems of linear equations. How can we use these components to efficiently find eigenvalues and eigenvectors of large matrices? This work is funded by NSF grant #1637559
    • Chenghong Wang, Zeinab S. Jalali, Caiwen Ding, Yanzhi Wang, and Sucheta Soundarajan. “A Fast and Effective Memristor-Based Method for Finding Approximate Eigenvalues and Eigenvectors of Non-Negative Matrices.” IEEE Annual Symposium on VLSI (ISVLSI). 2018. PDF
    • Ruizhe Cai, Ao Ren, Sucheta Soundarajan, and Yanzhi Wang. “A Low-Computation-Complexity, Energy-Efficient, and High-Performance Linear Program Solver Based on Primal Dual Interior Point Method Using Memristor Crossbars.” Nano Communication Networks. 2018. PDF
    • Ruizhe Cai, Ao Ren, Yanzhi Wang, Sucheta Soundarajan, Qinru Qiu, Bo Yuan, Paul Bogdan. A Low-Computation-Complexity, Energy-Efficient, and High-Performance Linear Program Solver Using Memristor Crossbars. IEEE International System-on-Chip Conference (SOCC). 2016. PDF.
  • Core structures in networks: k-cores and other notions of core structure are useful for identifying influential, central nodes. How can we identify such structures? And how robust are these structures to noise or attacks?
    • Ricky Laishram, Ahmet Erdem Sariyuce, Tina Eliassi-Rad, Ali Pinar, and Sucheta Soundarajan. “Measuring and Improving the Core Resilience of Networks.” The Web Conference (WWW). 2018. PDF
    • Priya Govindan, Chenghong Wang, Chumeng Xu, Hongyu Duan, and Sucheta Soundarajan. The k-peak decomposition: Mapping the global structure of graphs. International Conference on the World Wide Web (WWW). 2017. PDF