Search Results for author: Ambuj Singh

Found 15 papers, 11 papers with code

Learning Prototype Classifiers for Long-Tailed Recognition

no code implementations1 Feb 2023 Saurabh Sharma, Yongqin Xian, Ning Yu, Ambuj Singh

On the other hand, Prototype classifiers do not suffer from this shortcoming and can deliver promising results simply using Nearest-Class-Mean (NCM), a special case where prototypes are empirical centroids.

Long-tail Learning

Global Counterfactual Explainer for Graph Neural Networks

1 code implementation21 Oct 2022 Mert Kosan, Zexi Huang, Sourav Medya, Sayan Ranu, Ambuj Singh

One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.

Counterfactual Explanation Graph Classification

Deep Representations for Time-varying Brain Datasets

1 code implementation23 May 2022 Sikun Lin, Shuyun Tang, Scott Grafton, Ambuj Singh

Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications.

Incorporating Heterophily into Graph Neural Networks for Graph Classification

1 code implementation15 Mar 2022 Wei Ye, Jiayi Yang, Sourav Medya, Ambuj Singh

Graph neural networks (GNNs) often assume strong homophily in graphs, seldom considering heterophily which means connected nodes tend to have different class labels and dissimilar features.

Graph Classification

Graph Neural Diffusion Networks for Semi-supervised Learning

1 code implementation24 Jan 2022 Wei Ye, Zexi Huang, Yunqi Hong, Ambuj Singh

To solve these two issues, we propose a new graph neural network called GND-Nets (for Graph Neural Diffusion Networks) that exploits the local and global neighborhood information of a vertex in a single layer.

A Broader Picture of Random-walk Based Graph Embedding

1 code implementation24 Oct 2021 Zexi Huang, Arlei Silva, Ambuj Singh

Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks.

Graph Embedding Link Prediction

Event Detection on Dynamic Graphs

1 code implementation23 Oct 2021 Mert Kosan, Arlei Silva, Sourav Medya, Brian Uzzi, Ambuj Singh

In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.

Decision Making Event Detection

POLE: Polarized Embedding for Signed Networks

1 code implementation17 Oct 2021 Zexi Huang, Arlei Silva, Ambuj Singh

From the 2016 U. S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society.

Link Prediction Misinformation

Learning Deep Graph Representations via Convolutional Neural Networks

1 code implementation5 Apr 2020 Wei Ye, Omid Askarisichani, Alex Jones, Ambuj Singh

The learned deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood.

General Classification Graph Classification

Incorporating User's Preference into Attributed Graph Clustering

1 code implementation24 Mar 2020 Wei Ye, Dominik Mautz, Christian Boehm, Ambuj Singh, Claudia Plant

In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs).

Graph Clustering

Tree++: Truncated Tree Based Graph Kernels

1 code implementation23 Feb 2020 Wei Ye, Zhen Wang, Rachel Redberg, Ambuj Singh

At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.

Graph Similarity

Learning Heuristics over Large Graphs via Deep Reinforcement Learning

2 code implementations NeurIPS 2020 Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj Singh

Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.

Combinatorial Optimization Q-Learning +2

A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

no code implementations17 Oct 2015 Victor Amelkin, Ambuj Singh, Petko Bogdanov

In this work, we introduce Social Network Distance (SND) - a distance measure that quantifies the "cost" of evolution of one snapshot of a social network into another snapshot under various models of polar opinion propagation.

Event Detection

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