1 code implementation • 19 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.
no code implementations • 12 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.
1 code implementation • 20 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.
no code implementations • 15 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.
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.
no code implementations • 3 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.
no code implementations • 22 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.
no code implementations • 25 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.
3 code implementations • 23 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.
1 code implementation • 1 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.
Ranked #8 on Long-tail Learning on CIFAR-100-LT (ρ=10)
1 code implementation • 21 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.
1 code implementation • 23 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.
1 code implementation • 24 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.
1 code implementation • 24 Oct 2021 • Zexi Huang, Arlei Silva, Ambuj Singh
Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks.
1 code implementation • 23 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.
1 code implementation • 17 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.
no code implementations • ICLR 2021 • Arlei Lopes da Silva, Furkan Kocayusufoglu, Saber Jafarpour, Francesco Bullo, Ananthram Swami, Ambuj Singh
The flow estimation problem consists of predicting missing edge flows in a network (e. g., traffic, power and water) based on partial observations.
1 code implementation • 5 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.
1 code implementation • 24 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).
1 code implementation • 23 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.
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.
no code implementations • 30 Sep 2016 • Xuan-Hong Dang, Arlei Silva, Ambuj Singh, Ananthram Swami, Prithwish Basu
Detecting a small number of outliers from a set of data observations is always challenging.
no code implementations • 17 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.