Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved
state-of-the-art performance on graphs, but its success could typically hinge on careful …

Both node classification and link prediction are popular topics of supervised learning on the
graph data, but previous works seldom integrate them together to capture their …

Learning convolutional networks on graphs have been a popular topic for machine learning
on graph-structured data and achieved state-of-the-art results on various practical tasks …

Rank aggregation is a vital tool in facilitating decision-making processes that consider
multiple criteria or attributes. While in many applications, the available ranked lists are often …

Few-shot node classification poses a significant challenge for Graph Neural Networks
(GNNs) due to insufficient supervision and potential distribution shifts between labeled and …

Existing 2D image-based 3D model retrieval (IBMR) methods usually use the pseudo labels
as semantic guidance to reduce the domain-wise and class-wise feature distribution …

Rank aggregation is an obligatory operation for many tasks of democratic elections, product
recommendation, and gene identification. While the awareness of imperfect information in …

Due to a huge volume of information in many domains, the need for classification methods is
imperious. In spite of many advances, most of the approaches require a large amount of …

In spite of the huge advances in supervised learning, the common requirement for extensive
labeled datasets represents a severe bottleneck. In this scenario, other learning paradigms …

Recognizing emotions in textual conversations (ERC) is to identify the emotion of utterances
by considering conversational context. Current supervise-based ERC methods require a …