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.
2 code implementations • 24 Dec 2021 • Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal, Sayan Ranu
To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
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 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 • 15 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.
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.
1 code implementation • 7 Jun 2023 • Samidha Verma, Burouj Armgaan, Sourav Medya, Sayan Ranu
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design.
1 code implementation • 11 Feb 2023 • Zhu Wang, Sourav Medya, Sathya N. Ravi
Often, deep network models are purely inductive during training and while performing inference on unseen data.
Ranked #7 on Visual Question Answering on VQA v2 test-dev
1 code implementation • 14 Dec 2023 • Hao Tian, Sourav Medya, Wei Ye
Combinatorial Optimization (CO) problems over graphs appear routinely in many applications such as in optimizing traffic, viral marketing in social networks, and matching for job allocation.
no code implementations • 25 Oct 2019 • Palash Dey, Sourav Medya
These similarity measures turn out to be an important fundamental tool for many real world applications such as link prediction in networks, recommender systems etc.
Social and Information Networks Data Structures and Algorithms
no code implementations • 18 Dec 2020 • Yu-Wen Chen, Sourav Medya, Yi-Chun Chen
In this paper, we aim to identify and understand the impact of various factors on O3 formation and predict the O3 concentrations under different pollution-reduced and climate change scenarios.
no code implementations • 27 Feb 2021 • Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal
Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems.
no code implementations • 9 Sep 2021 • Debajyoti Kar, Mert Kosan, Debmalya Mandal, Sourav Medya, Arlei Silva, Palash Dey, Swagato Sanyal
Ensuring fairness in machine learning algorithms is a challenging and essential task.
no code implementations • 29 Sep 2021 • Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal, Sayan Ranu
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
1 code implementation • 25 Dec 2021 • Kartik Sharma, Samidha Verma, Sourav Medya, Arnab Bhattacharya, Sayan Ranu
In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.
no code implementations • 31 Jan 2022 • Sourav Medya, Mohammad Rasoolinejad, Yang Yang, Brian Uzzi
Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i. e., traditional hard data in most of the cases.
no code implementations • 2 Jun 2023 • Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, Sourav Medya
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems.
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 • 18 Oct 2023 • Rishi Shah, Krishnanshu Jain, Sahil Manchanda, Sourav Medya, Sayan Ranu
Second, we decouple the parameter space and the partition count making NeuroCUT inductive to any unseen number of partition, which is provided at query time.
no code implementations • 17 Jan 2024 • Giuseppe Stracquadanio, Sourav Medya, Stefano Quer, Debjit Pal
Then, it assigns an importance score to each operand in a design statement and uses that score for generating explanations for failures.
no code implementations • 8 Feb 2024 • Chirag Chhablani, Sarthak Jain, Akshay Channesh, Ian A. Kash, Sourav Medya
Our results reveals that computing Banzhaf values requires lower sample complexity in identifying the counterfactual explanations compared to other popular methods such as computing Shapley values.
no code implementations • 11 Mar 2024 • Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
no code implementations • 2 Apr 2024 • Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya
This progress extends to the field of patent analysis and innovation, where AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle such as classification, retrieval, and valuation prediction.