Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Medya, Sourav"'
Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its prediction performance. While selecting highly confident nodes has pr
Externí odkaz:
http://arxiv.org/abs/2410.09348
Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for hu
Externí odkaz:
http://arxiv.org/abs/2405.06917
Recent advancements in Artificial Intelligence (AI) and machine learning have demonstrated transformative capabilities across diverse domains. This progress extends to the field of patent analysis and innovation, where AI-based tools present opportun
Externí odkaz:
http://arxiv.org/abs/2404.08668
Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inherent randomness in data and model training errors can lead to unstable a
Externí odkaz:
http://arxiv.org/abs/2403.07185
Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, making it challenging to understand the reasoning behind their predictions
Externí odkaz:
http://arxiv.org/abs/2402.06030
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. It has several applications in diverse domains spanning s
Externí odkaz:
http://arxiv.org/abs/2312.12697
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. Due to their combinatorial nature, these problems are often
Externí odkaz:
http://arxiv.org/abs/2312.09086
Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. Conventional methods
Externí odkaz:
http://arxiv.org/abs/2310.11787
Autor:
Kosan, Mert, Verma, Samidha, Armgaan, Burouj, Pahwa, Khushbu, Singh, Ambuj, Medya, Sourav, Ranu, Sayan
Numerous explainability methods have been proposed to shed light on the inner workings of GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the interrogative aspects of these evaluations lack diversity. As a result,
Externí odkaz:
http://arxiv.org/abs/2310.01794
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot provide understandable explanations for their predictions. To address th
Externí odkaz:
http://arxiv.org/abs/2306.04835