Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Gene regulatory network (GRN) inference, a longstanding challenge in computational
biology, aims to construct GRNs from genomic data. Graph Neural Networks (GNNs) are …

Semantic text matching plays a vital role in diverse domains, such as information retrieval,
question answering, and recommendation. However, longer texts present challenges …

Graphs are a foundational way to represent scenarios where objects interact in pairs.
Recently, graph neural networks (GNNs) have become widely used for modeling simple …

Nowadays, the internet is the most used communication environment, and therefore it
becomes very important to try to determine the behavior of users regarding internet use. Due …

Recognizing the coreference relationship between different event mentions in the text (ie,
event coreference resolution) is an important task in natural language processing. It helps to …

Currently, the aggregation capabilities of neighboring nodes in Graph Neural Networks
(GNNs) have been demonstrated as effective in detecting user privacy co-disclosure across …

Trust plays an essential role in an individual's decision-making. Traditional trust prediction
models rely on pairwise correlations to infer potential relationships between users. However …

Edge prediction is a fundamental challenge in network science, with broad applications,
notably in social networks. It plays a crucial role in unveiling complex system dynamics by …

We conducted our literature review by in-depth reading, interpreting, and categorizing
articles addressing the problem of generating a low-dimensional representation of a …