Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative
and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph …

Disentangled representation learning has recently attracted a significant amount of attention,
particularly in the field of image representation learning. However, learning the disentangled …

As an essential task of talent recruitment, person-job fit aims to measure the matching
degree between talent qualifi-cation and the job requirements of a position. Existing studies …

Abstract The task of Grounded Video Description (GVD) is to generate sentences whose
objects can be grounded with the bounding boxes in the video frames. Existing works often …

The task of RDF-to-text generation is to generate a corresponding descriptive text given a
set of RDF triples. Most of the previous approaches either cast this task as a sequence-to …

Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics
questions. Previous natural language processing researches have proven the effectiveness …

Deep generative models for graphs have recently achieved great successes in modeling
and generating graphs for studying networks in biology, engineering, and social sciences …

The visual question generation (VQG) task aims to generate human-like questions from an
image and potentially other side information (eg, answer type). Previous works on VQG fall …