no code implementations • 4 Oct 2021 • Deepayan Chakrabarti
Experiments on $25$ real-world datasets and three standard loss functions show that RoLin broadly outperforms both dimensionality reduction and regularization.
no code implementations • NeurIPS 2018 • Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
People belong to multiple communities, words belong to multiple topics, and books cover multiple genres; overlapping clusters are commonplace.
no code implementations • 1 Sep 2017 • Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
We consider the problem of estimating community memberships of nodes in a network, where every node is associated with a vector determining its degree of membership in each community.
no code implementations • ICML 2017 • Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
The problem of finding overlapping communities in networks has gained much attention recently.
no code implementations • NeurIPS 2015 • Purnamrita Sarkar, Deepayan Chakrabarti, Peter J. Bickel
Link prediction and clustering are key problems for network-structureddata.
no code implementations • 30 Jan 2014 • Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy
We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels.
no code implementations • 27 Jun 2012 • Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time.
no code implementations • 6 Sep 2011 • Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
We propose a nonparametric approach to link prediction in large-scale dynamic networks.
no code implementations • NeurIPS 2008 • Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, Eli Upfal
In our model, arms have (stochastic) lifetime after which they expire.