Search Results for author: Ambuj Singh

Found 24 papers, 16 papers with code

Link Prediction without Graph Neural Networks

3 code implementations23 May 2023 Zexi Huang, Mert Kosan, Arlei Silva, Ambuj Singh

Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications.

Attribute Graph Learning +1

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 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

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 Counterfactual Explanation +2

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

Fragment-based Pretraining and Finetuning on Molecular Graphs

1 code implementation NeurIPS 2023 Kha-Dinh Luong, Ambuj Singh

Borrowing techniques from recent work on principal subgraph mining, we obtain a compact vocabulary of prevalent fragments from a large pretraining dataset.

Contrastive Learning Property Prediction +1

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

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

DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization

1 code implementation20 Dec 2023 Aritra Bhowmick, Mert Kosan, Zexi Huang, Ambuj Singh, Sourav Medya

Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph.

Clustering Graph Clustering +2

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.

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 Prototype Classifiers for Long-Tailed Recognition

1 code implementation1 Feb 2023 Saurabh Sharma, Yongqin Xian, Ning Yu, Ambuj Singh

In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR.

Long-tail Learning

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).

Attribute Clustering +1

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

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.

Attribute Task 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

Robust Ante-hoc Graph Explainer using Bilevel Optimization

no code implementations25 May 2023 Mert Kosan, Arlei Silva, Ambuj Singh

Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions.

Attribute Bilevel Optimization +1

Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions

no code implementations22 Aug 2023 Rhys Tracy, Haotian Xia, Alex Rasla, Yuan-Fang Wang, Ambuj Singh

Our results show that the use of GNNs with our graph encoding yields a much more advanced analysis of the data, which noticeably improves prediction results overall.

Type prediction

GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking

no code implementations3 Oct 2023 Mert Kosan, Samidha Verma, Burouj Armgaan, Khushbu Pahwa, Ambuj Singh, Sourav Medya, Sayan Ranu

Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques.

Benchmarking counterfactual

XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making

no code implementations15 Nov 2023 Zichen Chen, Jianda Chen, Mitali Gaidhani, Ambuj Singh, Misha Sra

The explanation component includes a why-choose explanation, a why-not-choose explanation, and a set of reason-elements that underlie the LLM's decision.

Decision Making Graph Attention +4

Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees

no code implementations12 Feb 2024 Sean Jaffe, Alexander Davydov, Deniz Lapsekili, Ambuj Singh, Francesco Bullo

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty.

Electioneering the Network: Dynamic Multi-Step Adversarial Attacks for Community Canvassing

1 code implementation19 Mar 2024 Saurabh Sharma, Ambuj Singh

We show that the MBACC problem is NP-Hard and propose Dynamic Multi-Step Adversarial Community Canvassing (MAC) to address it.

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