7 accepted papers
Graph augmentation is a cornerstone of effective graph contrastive learning, yet existing methods often rely on random designed perturbations, which may distort latent semantics and impair representation quality. In this work, we argue that semantic consistency can be effectively approximated by low
Despite the significant advances in RGB-D scene recognition, there are several major limitations that need further investigation. For example, simply extracting modal-specific features neglects the complex relationships among multiple modalities of features. Moreover, cross-modal features have not b…
Session-based recommendation (SBR) aims to predict the user’s action at the next timestamp according to an anonymous yet short interaction sequence (i.e., session). Almost all the existing SBR solutions for user preference are only based on the current session without exploiting the high-order relat…
From the perspective of the spatial domain, Graph Convolutional Network (GCN) is essentially a process of iteratively aggregating neighbor nodes. However, the existing GCNs using simple average or sum aggregation may neglect the characteristics of each node and the topology between nodes, resulting…
The human recognition system has presented the remarkable ability to effortlessly learn novel knowledge from only a few trigger events based on prior knowledge, which is called insight learning. Mimicking such behavior on Knowledge Graph Reasoning (KGR) is an interesting and challenging research pro…
Existing graph contrastive methods have benefited from ingenious data augmantations and mutual information estimation operations that are carefully designated to augment graph views and maximize the agreement between representations produced at the aftermost layer of two view networks. However, the…
Graph convolutional network (GCN) is a novel framework that utilizes a pre-defined Laplacian matrix to learn graph data effectively. With its powerful nonlinear fitting ability, GCN can produce high-quality node embedding. However, generalized GCN can only handle static graphs, whereas a large numbe…