Networks

We learn distributed vector representations of the vertices in large-scale networks that preserve structural and semantic similarity of the vertices in the embedding space

About

The group is active in a wide range of Networks related areas. The research focus of the group spans from complex network modeling to the lower dimensional representation of networks with recent techniques, such as deep learning. The group is highly involved in the study of complex network structures such as Multiplex Networks, Hypergraphs, large graphs etc. More details about our work can be found on this page

People

Ph.D Scholars

Project Associates

Publications

  1. DCEIL: Distributed Community Detection with the CEIL Score A. Jain, R. Nasre, B. Ravindran in the Proceedings of the Nineteenth IEEE International Conference on High Performance Computing and Communication HPCC 2017 (2017)
  2. Role Discovery in Graphs Using Global Features: Algorithms, Applications and a Novel Evaluation Strategy Pratik Vinay Gupte, Balaraman Ravindran, Srinivasan Parthasarathy Data Engineering (ICDE), 2017 IEEE 33rd International Conference on (2017) 771--782
  3. HEMI: Hyperedge Majority Influence Maximization Varun Gangal, Balaraman Ravindran, Ramasuri Narayanam arXiv preprint arXiv:1606.05065 (2016)
  4. Measuring network centrality using hypergraphs Sanjukta Roy, Balaraman Ravindran Proceedings of the Second ACM IKDD Conference on Data Sciences (2015) 59--68
  5. Near optimal strategies for targeted marketing in social networks Ramakumar Pasumarthi, Ramasuri Narayanam, Balaraman Ravindran Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (2015) 1679--1680
  6. Commit: A scalable approach to mining communication motifs from dynamic networks Saket Gurukar, Sayan Ranu, Balaraman Ravindran Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (2015) 475--489
  7. Extended Discriminative Random Walk: A Hypergraph Approach to Multi-View Multi-Relational Transductive Learning. Sai Nageswar Satchidanand, Harini Ananthapadmanaban, Balaraman Ravindran IJCAI (2015) 3791--3797
  8. CEIL: a scalable, resolution limit free approach for detecting communities in large networks Vishnu Sankar, Balaraman Ravindran, S Shivashankar Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
  9. Multi-label collective classification in multi-attribute multi-relational network data Priyesh Vijayan, Shivashankar Subramanian, Balaraman Ravindran Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on (2014) 509--514
  10. Temporal analysis of telecom call graphs Saket Gurukar, Balaraman Ravindran Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on (2014) 1--6
  11. Studying Indian Railways Network using hypergraphs Sai Nageswar Satchidanand, Siddharth Kumar Jain, Amit Maurya, Balaraman Ravindran Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on (2014) 1--6