Workshop on
Complex and Social
Networks

Complex Network Research Group, IIT Kharagpur, is organising a one-day workshop highlighting various ongoing cutting edge research in the area of social and complex networks. Leading researchers from academia and industry will participate. This will be specially helpful for students to get aware of the ongoing cutting-edge research going on in the field of computer science, and will also be an opportunity to interact with leaders in this field. The workshop will also feature a focused panel discussion on cutting-edge research in computer science and its scope in industry as well as academia.
Date: 15th March, 2017
Venue: Gargi Auditorium

Program Schedule

Download Schedule
08.30 AM - 09.00 AM Registration and Breakfast
09:00 AM - 09.10 AM Opening and Introduction by Prof. Sudeshna Sarkar, HoD, CSE
09:10 AM - 09.30 AM CNeRG Group Presentation, Pawan Goyal
09.30 AM - 10.30 AM Frank Schweitzer, Prof. ETH Zurich, Spreading influence in social networks: From link-centric to node-centric models
10.30 AM - 10.45 AM Tea Break
10.45 AM - 11.30 AM Manish Gupta, Researcher, Microsoft, Outlier Detection for Complex Networks: Top-K Interesting Subgraph Discovery in Complex Networks
11.30 AM - 12.15 PM Laxmidhar Behera, Prof. IIT Kanpur, Deep Learning: Deep Learning through Some Illustrative Applications
12.15 PM - 12.45 PM CNeRG: Ongoing Projects, Upcoming Projects, Sandip Chakraborty
12.45 PM - 01:45 PM Lunch
01.45 PM - 02:30 PM Poster Session
02.30 PM - 03.30 PM Panel Discussions: Panel Topic: How to sell your thesis to industry.
03.30 PM - 03.45 PM Closing/Conclusion

Speakers

Title: Spreading influence in social networks: From link-centric to node-centric models

Abstract: Epidemic spreading on complex networks is well studied because nodes follow a rather simple dynamics. Thus, the focus is mostly on how the network topology impacts the spreading process. However, modeling the spread of, e.g., emotions in online social networks requires us to have more refined models of the node dynamics, to calculate cascades of spreading influence. We capture the node dynamics by means of a data-driven modeling approach that allows us to test, and to calibrate, assumptions about the user behavior. In my talk, I present different examples of how to complement the topological perspective by a node-centric perspective that considers costs and benefits, emotional responses or information processing of users.

Speakers Bio: Frank Schweitzer has been Full Professor for Systems Design at ETH Zurich, since 2004. He is also associated member of the Department of Physics at the ETH Zurich.The research of Frank Schweitzer focuses on applications of complex systems theory in the dynamics of social and economic organizations. His methodological approach can be best described as data-driven modeling, i.e., it combines the insights from big data analysis with the power of agent-based computer simulations and the strength of rigorous mathematical models. Frank Schweitzer is a founding member of the ETH Risk Center and Editor-in-Chief of ACS - Advances in Complex Systems and EPJ Data Science.
Title: Top-K Interesting Subgraph Discovery in Complex Networks

Abstract: In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. Many problems on such networks can be mapped to an underlying critical problem of discovering top-K subgraphs of entities with rare and surprising associations. Answering such subgraph queries efficiently involves two main challenges: (1) computing all matching subgraphs which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the subgraphs. Previous work on the matching problem can be harnessed for a naive ranking-after-matching solution. However, for large graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only the top-K matches are desired. In this paper, we address the two challenges of matching and ranking in top-K subgraph discovery as follows. First, we introduce two index structures for the network: topology index, and graph maximum metapath weight index, which are both computed offline. Second, we propose novel top-K mechanisms to exploit these indexes for answering interesting subgraph queries online efficiently. Experimental results on several synthetic datasets and the DBLP and Wikipedia datasets containing thousands of entities show the efficiency and the effectiveness of the proposed approach in computing interesting subgraphs.

Speakers Bio: Manish Gupta is a Senior Applied Researcher at Microsoft India R&D Private Limited at Hyderabad, India. He is also an Adjunct Faculty at International Institute of Information Technology, Hyderabad and a visiting faculty at Indian School of Business, Hyderabad. He received his Masters in Computer Science from IIT Bombay in 2007 and his Ph.D. from the University of Illinois at Urbana-Champaign in 2013. Before this, he worked for Yahoo! Bangalore for two years. His research interests are in the areas of web mining, data mining and information retrieval. He has published more than 50 research papers in reputed referred journals and conferences. He has also co-authored two books: one on Outlier Detection for Temporal Data and another one on Information Retrieval with Verbose Queries.
Title: Deep Learning through Some Illustrative Applications

Abstract: Deep learning has caught the imagination of researchers due to its recent successes. In this talk, a brief overview of the subject will be followed by the detailed discussion on restricted Boltzmann Machine. A couple of applications in BCI and visual perception will be presented. Our experience in Amazon Picking Challenge 2016 will be enumerated while highlighting RCNN based visual perceptions.

Speakers Bio: Laxmidhar Behera received the BSc (engineering) and MSc (engineering) degrees from NIT Rourkela in 1988 and 1990, respectively. He received the PhD degree from IIT Delhi in1996. He is currently working as a professor in the Department of Electrical Engineering, IIT Kanpur. He has worked as an assistant professor at BITS Pilani during 1995-1999 and pursued postdoctoral studies in the German National Research Center for Information Technology, GMD, Sank Augustin, Germany, during 2000-2001. He joined the Intelligent Systems Research Center (ISRC), University of Ulster, United Kingdom, as a reader on sabbatical from IIT Kanpur during 2007-2009. He has also worked as a visiting researcher/professor at FHG, Germany,and ETH, Zurich, Switzerland. He has more than 200 papers to his credit published in refereed journals and presented in conference proceedings. His book on Intelligent Systems and Control published by Oxford University Press is in 5th reprint and is being prescribed as graduate level text book in many Universities across the world. He has supervised 13 PhD students to completion and is currently supervising 12 PhD students. His research interests include intelligent control, robotics, semantic signal/music processing, neural networks, control of cyber-physical systems, and cognitive modeling. His team secured 5th position on the Amazon Picking Challenge 2016 and has reached the final of Amazon Robotics Challenge 2017 that will be held in Japan.

Panel Discussion: How to sell your thesis to industry.

Panelists:

Amit Saha, Cisco

Tridib Mukherjee, Xerox

Subir Ray, Iotmize, USA

Prof. Suman Chakraborty, Associate Dean, SRIC, IITKGP

Prof. Animesh Mukherjee, CSE, IITKGP (Moderator)

Registration for Participation

Contact Details


Dr. Pawan Goyal

Assistant Professor

Department of Computer Science and Engineering

Indian Institute of Technology, Kharagpur