Sunil Kumar Maurya

Graphs arise naturally in numerous situations, including social graphs, transportation graphs, web graphs, protein graphs, etc. One of the important problems in these settings is to identify which nodes are important in the graph and how they affect the graph structure as a whole. Betweenness centrality and closeness centrality are two commonly used node ranking measures to find out influential nodes in the graphs in terms of information spread and connectivity. Both of these are considered as s

Betweenness centrality is an important measure to find out influential nodes in networks in terms of information spread and connectivity. However, the exact calculation of betweenness centrality is computationally expensive. Although researchers have proposed approximation methods, they are either less efficient, or suboptimal, or both. In this paper, we present a Graph Neural Network(GNN) based inductive framework which uses constrained message passing of node features to approximate betweennes

Abstract Graphs help to define the relationships between entities in the data. These relationships, represented by edges, often provide additional context information which can be utilised to discover patterns in the data. Graph Neural Networks (GNNs) employ the inductive bias of the graph structure to learn and predict on various tasks. The primary operation of graph neural networks is the feature aggregation step performed over neighbours of the node based on the structure of the graph. In add

Graph Neural Networks have emerged as a useful tool to learn on the data by applying additional constraints based on the graph structure. These graphs are often created with assumed intrinsic relations between the entities. In recent years, there have been tremendous improvements in the architecture design, pushing the performance up in various prediction tasks. In general, these neural architectures combine layer depth and node feature aggregation steps. This makes it challenging to analyze the

The fundamental operation of Graph Neural Networks (GNNs) is the feature aggregation step performed over neighbors of the node based on the structure of the graph. In addition to its own features, the node gets additional combined features from its neighbors for each hop. These aggregated features help define the similarity or dissimilarity of the nodes with respect to the labels and are useful for tasks like node classification. However, in real-world data, features of neighbors at different ho

In static temporal networks, the Earliest Arrival Time (EAT) problem is to calculate the earliest possible time of arrival at a set of vertices from a given source vertex. Applications of the EAT problem include designing efficient evacuation planning in dynamic scenarios, optimal journey planning in transport networks, and optimal flow management in supply chains. There exist several solutions for the EAT problem in the literature, however, there is limited work on GPU-based solutions to levera

Attached is pre-processed data splits for snap-patents dataset. Relevant github repository is https://github.com/sunilkmaurya/DualNetGNN_large Paper: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.12166

Robotics being a major community in the upcoming progressive terminology of modern era, it plays an important role in cutting down manpower in certain areas. Nowadays robotics is being utilized in numerous areas and is playing effective and beneficial role in the respective. In this paper such kind of walking robots are being discussed whose presence makes things easy for humans specially certain jobs where manpower is excessively needed. Manpower being utilized to uplift things or transport thi

Pre-processed data for 5 large graph datasets are attached. Relevant GitHub link: https://github.com/sunilkmaurya/DualNetGNN_large Paper: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.12166