Apurba Das successfully completed his PhD preliminary exam. He is working on the topic “Maintaining Dense Structure in a Dynamic Graph”. Congrats, Apurba!
Our paper titled “Butterfly Counting in Bipartite Networks”, by Vahid Sanei, Ahmet Erdem Sariyuce, and Srikanta Tirthapura has been accepted to appear in the ACM International Conference on Knowledge Discovery and Data Mining (KDD), 2018. This presents a fast method to count subgraphs in large bipartite networks, which are often used to model relationships between two types of entities, say, users and products that they buy. Congrats, Vahid!
Our demonstration titled “HYDRA: A Dynamic Big Data Regenerator” has been accepted to International Conference on Very Large Databases (VLDB) 2018. This system is work with the team at IISc Bangalore: Anupam Sanghi, Raghav Sood, Dharmendra Singh, Jayant Haritsa.
Yesta successfully defended his thesis that examines how to reduce the number of labeling steps in data stream mining. Congrats, Yesta!
“V2V: Vector Embedding of a Graph and Applications”, by Trong Nguyen and Srikanta Tirthapura has been accepted to the Workshop on the Intersection of Graph Algorithms and Machine Learning (GraML 2018). Congrats Trong!
“Onion Curve: A Space Filling Curve with Near-Optimal Clustering” by Pan Xu, Cuong Nguyen, and Srikanta Tirthapura accepted to IEEE ICDE (International Conference on Data Engineering) 2018.
“Learning Graphical Models from a Distributed Stream” by Yu Zhang, Srikanta Tirthapura, and Graham Cormode has been accepted to IEEE ICDE (International Conference on Data Engineering) 2018. Congrats to PhD student Yu Zhang!
“Incremental Maintenance of Maximal Bicliques in a Dynamic Bipartite Graph”, by Apurba Das and Srikanta Tirthapura has been accepted to the journal IEEE Transactions on Multi-Scale Computing Systems. Congrats to PhD student Apurba Das.
Our paper titled “Scalable and Dynamic Regeneration of Big Data Volumes” has been accepted to 21st International Conference on Extending Database Technology (EDBT) 2018. This is joint work with collaborators in the Database Group at the Indian Institute of Science, Bangalore.
Our paper titled “Work-Efficient Parallel Union-Find” has been accepted to the journal Concurrency and Computation: Practice and Experience. The authors are Simsiri, Tangwongsan, Tirthapura, and Wu.
This paper presents a shared-memory parallel algorithm for the fundamental “Union-Find” problem for maintaining equivalence classes. The uniqueness of this solution is that it is the first parallel algorithm whose total work across all processors is of the same order as the computational cost of the best sequential algorithm for union-find, which uses “path compression” (whose now-famous analysis by Tarjan has shown it to be near-constant time per operation).