Search Results for author: Madhav Nimishakavi

Found 9 papers, 6 papers with code

Graph-based Modeling of Online Communities for Fake News Detection

1 code implementation14 Aug 2020 Shantanu Chandra, Pushkar Mishra, Helen Yannakoudakis, Madhav Nimishakavi, Marzieh Saeidi, Ekaterina Shutova

Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as the demographic traits of users who interact with them.

Fake News Detection

HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

1 code implementation NeurIPS 2019 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.

HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

1 code implementation7 Sep 2018 Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.

Lovasz Convolutional Networks

1 code implementation29 May 2018 Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar

We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.

Multi-class Classification

Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

no code implementations18 Feb 2018 Madhav Nimishakavi, Bamdev Mishra, Manish Gupta, Partha Talukdar

Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time.

A dual framework for low-rank tensor completion

no code implementations NeurIPS 2018 Madhav Nimishakavi, Pratik Jawanpuria, Bamdev Mishra

One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization.

Riemannian optimization

Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation

1 code implementation ACL 2018 Madhav Nimishakavi, Partha Talukdar

Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text.

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