This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.
But the challenges associated with the separation of functional modules, slows down the migration of a monolithic code into microservices.
Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation.
Designing an unsupervised loss function to train a GNN and extract communities in an integrated manner is a fundamental challenge.
Real world networks often come with (community) outlier nodes, which behave differently from the regular nodes of the community.
Towards this end, we propose a graph classification algorithm called SubGattPool which jointly learns the subgraph attention and employs two different types of hierarchical attention mechanisms to find the important nodes in a hierarchy and the importance of individual hierarchies in a graph.
Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space.
Then we propose to use graph convolution on the line graph of a hypergraph.
To the best of our knowledge, this is the first direct unsupervised approach for edge embedding in homogeneous information networks, without relying on the node embeddings.
Along with attention over the subgraphs, our pooling architecture also uses attention to determine the important nodes within a level graph and attention to determine the important levels in the whole hierarchy.
We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique.
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
In this work, we propose a nonnegative matrix factorization based optimization framework, namely FSCNMF which considers both the network structure and the content of the nodes while learning a lower dimensional vector representation of each node in the network.
Social and Information Networks