Workshop on Distributed Systems for Coordinated Disaster Management (CORDIM 2013), held in conjunction with the International Conference on Distributed Computing and Networking (ICDCN 2013), Mumbai, India, January 2013.

Dynamics On and Of Complex Networks

Dynamics On and Of Complex Networks – VI, A Satellite Workshop of European Conference on Complex Systems (ECCS 2013), Barcelona, Spain, September 2013.

Short-term Course and Workshop on Machine Learning and Complex Networks, IIT Kharapur

Machine Learning is an interdisciplinary area dealing with modeling of real-life systems, and robust learning of model parameters. Complex networks deals with real-life networks such as Social Networks, Biological Networks, Computer Networks, etc. which have many non-trivial properties. These two areas have borrowed heavily from each other. On one hand, techniques in machine learning are used to learn the dynamics of complex networks as well as learn the dynamics of various functionalities performed on the networks. On the other hand, many machine learning techniques and applications involve systems which learn over graphs/networks, e.g. distributed learning using cluster of machines, learning over graphical models (learning of CRFs, structured learning, etc), deep learning etc.

In this workshop, we are interested in studying the fundamental concepts underlying this area and also discuss the potential research directions and applications.

NetSciCom 2016, San Fransisco, CA, USA

Network Science attracts the attention of a large number of researchers from across various disciplines, mainly due to its applicability in modelling the structure and dynamics of a wide variety of large-scale complex networks, ranging from genetic pathways and ecological networks to the Internet, WWW, peer-to-peer networks, blogs and online social networks, etc. Moreover, Network science has compelling applications in the field of (say) computer communication networks, electric power grid networks, transportation networks, social networks, and biological networks.

Social Networking Workshop 2016

To facilitate cross-disciplinary discussion of relevance to social networking, involving novel ideas and applications, and experimental results. This workshop provides an opportunity to compare and contrast the ethological approach to social behaviour in human with web-based evidence of social interaction, perceptual learning, information granulation, the behaviour of humans and affinities between web-based social networks.

TextGraphs 2016: Graph-based Methods for Natural Language Processing, San Diego, California, USA

TextGraphs workshops have exposed and encouraged the synergy between the field of Graph Theory (GT) and Natural Language Processing (NLP). The mix between the two started small, with graph theoretical framework providing efficient and elegant solutions for NLP applications. Solutions focused on single documents for part-of-speech tagging, word sense disambiguation, and semantic role labelling, and got progressively larger with ontology learning and information extraction from large text collections. Nowadays, the solutions have reached web scale through new fields of research that focus on information propagation in social networks, rumor proliferation, e-reputation, multiple entity detection, language dynamics learning, and future events prediction to name a few.


Workshop on Social Computing (WoSC 2012), IIT Kharagpur, India, October 2012.