Classical ML techniques assume the data to be iid, but the real world data is inherently relational and can generally be represented using graphs or some variants of them. The importance of modelling structured data is evident from its increasing presence: WWW, social networks, organizational network, image, protein sequence, relational data etc. This field has been recently receiving a lot of attention in the community under different themes depending on the problem addressed and the nature of solution. Researchers in different areas have proposed very useful and successful frameworks:
Learning with network data involves many building blocks:
There is huge research progress on these subtasks in each area individually. Also, workshops are held for each field such as SRL, ILP, StarAI, GBR, GDM. Collective inference is a common factor to all these subtasks, and notably it has attained huge progress in individual areas. Some of the future/past workshops on collective inference include "CVPR WS on Inference in Graphical Models and Structured Potentials", "Propagation Algorithms on Graphs with Cycles: Theory and Applications", "Approximate inference-How far have we come?", "Approximate Learning of Large Scale Graphical Models: Theory and Applications". We believe the current situation provides us with an opportunity for attempts at synthesis, forming a common core of problems and ideas, and crosspollinating across subareas. There have been few attempts, and a notable success in the MLG series. MLG addresses all general aspects of mining and learning with graphs, whereas CoLISD focuses on the within-network learning and inference tasks with special emphasis on collective inference. Inspired by the success of MLG, this workshop will attempt to reach out to different groups which work on the same theme and to explore together how to reach the goals w.r.t within-network learning and inference for each subfield mentioned above.
Following up on the successful conduct of the CoLISD workshop last year at ECML PKDD, we propose to organize the second edition of the workshop this year. The first edition of the workshop succeeded in bringing together researchers from various communities who look at different aspects of learning with structured data. For many of the participants it was the first time they were seriosuly looking at approaches from other disciplines. The wide spread feeling was that the workshop should be continued since there was scope for much crossfertilization. We are currently working on a special issue of MLJ based on the outcome of the first workshop.