Attributed graph clustering, which learns node representation from attribute and topological for is a fundamental challenging task multimedia network-structured data analysis. Recently, contrastive learning (GCL)-based methods have obtained impressive clustering performance on this task. Nevertheless, there still remain some limitations to be solved: 1) most existing fail consider the self-cons...

The lack of entity label values is one the problems faced by application Knowledge Graph. method automatically assigning still has shortcomings, such as costing more resources during training, leading to inaccurate value assignment because lacking semantics. In this paper, oriented domain-specific Graph, based on situation that initial all triples are completely unknown, an Entity Label Value A...

Graph neural networks (GNNs) have achieved state-of-the-art performance for node classification on graphs. The vast majority of existing works assume that genuine labels are always provided training. However, there has been very little research effort how to improve the robustness GNNs in presence label noise. Learning with noise primarily studied context image classification, but these techniq...

Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies how to sufficiently leverage limited but valuable label information. Most of classical GNNs solely use known labels for computing loss at output. In recent years, several methods designed additionally utilize input. One part augment features via concatenating or adding t...

This paper describes a parameter estimation method for multi-label classification that does not rely on approximate inference. It is known that multi-label classification involving label correlation features is intractable, because the graphical model for this problem is a complete graph. Our solution is to exploit the sparsity of features, and express a model structure for each object by using...

Given a graph G = (V,E) with non-negative edge lengths whose vertices are assigned a label from L = {λ1, . . . , λl}, we construct a compact distance oracle that answers queries of the form: “What is δ(v, λ)?”, where v ∈ V is a vertex in the graph, λ ∈ L a vertex label, and δ(v, λ) is the distance (length of a shortest path) between v and the closest vertex labeled λ in G. We formalize this nat...

Predicting emotion categories, such as anger, joy, and anxiety, expressed by a sentence is challenging due to its inherent multi-label classification difficulty and data sparseness. In this paper, we address above two challenges by incorporating the label dependence among the emotion labels and the context dependence among the contextual instances into a factor graph model. Specifically, we rec...