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S. Ravi

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2 papers

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AAAI Conference 2020 Conference Paper

Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems

Abhijin Adiga
Chris Kuhlman
Madhav Marathe
S. Ravi
Daniel Rosenkranz
Richard Stearns
Anil Vullikanti

Using a discrete dynamical system model for a networked social system, we consider the problem of learning a class of local interaction functions in such networks. Our focus is on learning local functions which are based on pairwise disjoint coalitions formed from the neighborhood of each node. Our work considers both active query and PAC learning models. We establish bounds on the number of queries needed to learn the local functions under both models. We also establish a complexity result regarding efﬁcient consistent learners for such functions. Our experimental results on synthetic and real social networks demonstrate how the number of queries depends on the structure of the underlying network and number of coalitions.

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AAAI Conference 2017 Conference Paper

A Framework for Minimal Clustering Modification via Constraint Programming

Chia-Tung Kuo
S. Ravi
Thi-Bich-Hanh Dao
Christel Vrain
Ian Davidson

Consider the situation where your favorite clustering algorithm applied to a data set returns a good clustering but there are a few undesirable properties. One adhoc way to ﬁx this is to re-run the clustering algorithm and hope to ﬁnd a better variation. Instead, we propose to not run the algorithm again but minimally modify the existing clustering to remove the undesirable properties. We formulate the minimal clustering modiﬁcation problem where we are given an initial clustering produced from any algorithm. The clustering is then modiﬁed to: i) remove the undesirable properties and ii) be minimally different to the given clustering. We show the underlying feasibility sub-problem can be intractable and demonstrate the ﬂexibility of our constraint programming formulation. We empirically validate its usefulness through experiments on social network and medical imaging data sets.

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