GGNNs : Generalizing GNNs using Residual Connections and Weighted Message Passing

Many real-world phenomena can be modeled as a graph, making them extremely valuable due to their ubiquitous presence. GNNs excel at capturing those relationships and patterns within these graphs, enabling effective learning and prediction tasks. GNNs are constructed using Multi-Layer Perceptrons (MLPs) and incorporate additional layers for message passing to facilitate the flow of features among nodes. It is commonly believed that the generalizing power of GNNs is attributed to the message-passing mechanism between layers, where nodes exchange information with their neighbors, enabling them to effectively capture and propagate information across the nodes of a graph. Our technique builds on these results, modifying the message-passing mechanism further: one by weighing the messages before accumulating at each node and another by adding Residual connections. These two mechanisms show significant improvements in learning and faster convergence

Agree To Disagree

How frequently do individuals thoroughly review terms and conditions before proceeding to register for a service, install software, or access a website? The majority of internet users do not engage in this practice. This trend is not surprising, given that terms and conditions typically consist of lengthy documents replete with intricate legal terminology and convoluted sentences. In this paper, we introduce a Machine Learning-powered approach designed to automatically parse and summarize critical information in a user-friendly manner. This technology focuses on distilling the pertinent details that users should contemplate before committing to an agreement.