Search Results for author: Vijay Lingam

Found 9 papers, 2 papers with code

Probing Graph Representations

1 code implementation7 Mar 2023 Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski

Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models.

A Piece-wise Polynomial Filtering Approach for Graph Neural Networks

1 code implementation7 Dec 2021 Vijay Lingam, Chanakya Ekbote, Manan Sharma, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam

We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.

Node Classification

Effective Polynomial Filter Adaptation for Graph Neural Networks

no code implementations29 Sep 2021 Vijay Lingam, Chanakya Ajit Ekbote, Manan Sharma, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam

We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.

Node Classification

User Embedding based Neighborhood Aggregation Method for Inductive Recommendation

no code implementations15 Feb 2021 Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer, Ramakrishna Bairi

CF-LGCN-U models naturally possess the inductive capability for new items, and we propose a simple solution to generalize for new users.

Collaborative Filtering

HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification

no code implementations19 Aug 2020 Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer, Ram Bairi, Vijay Lingam

We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features.

General Classification Graph Embedding +2

A Graph Convolutional Network Composition Framework for Semi-supervised Classification

no code implementations8 Apr 2020 Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer

Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification.

Classification General Classification +1

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