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Graph Neural Networks (GNNs) have achieved remarkable success in graph-based tasks. However, learning unbiased node representations under class-imbalanced training data remains challenging. Existing solutions may face overfitting due to extensive reuse of ...

In recent years, graph embedding techniques have exhibited great potential for various downstream tasks, which can leverage both topological structures and the temporal dependencies of nodes in their representations, leading to remarkable achievements. ...

Multi-Modal Large Language Models (MM-LLMs) have demonstrated powerful reasoning abilities in various visual question-answering tasks. However, they face the challenge of lacking rigorous reasoning and precise arithmetic, when solving geometry questions. ...

Social recommendation systems leverage the social relations among users to deal with the inherent cold-start problem in user-item interactions. However, previous models only treat the social graph as the static auxiliary to the user-item interaction ...

Graph Neural Network (GNN) models are recently proposed to process the graph-structured data for the learning tasks on graphs, e.g., node classification, link prediction, and so on. This work focuses on the graph classification task, aiming to ...

• The module-based graph pooling (MGPool) framework obtains the graph representation by three stages from bottom to top: node, module and graph.
• MGPool considers information from both graph and module views during node encoding.
• An ...

In this paper, we propose a novel self-supervised Gaussian Restricted Boltzmann Machine with contrastive learning (CL-GRBM), which fuses contrastive representation learning and contrastive divergence to optimize and enhance the representation of ...

Generative art has drawn increased attention in recent AI applications. Traditional approaches of robot calligraphy have faced challenges in achieving style consistency, line smoothness and high-quality structural uniformity. To address the ...

Multi-view subspace clustering (MSC) has gained significant popularity due to its ability to overcome noise and bias present in single views by fusing information from multiple views. MSC enhances the accuracy and robustness of clustering. ...

Density Peak Clustering (DPC) is a highly effective density-based clustering algorithm, but its scalability is limited by the expensive Density Peak Estimation (DPE) step. To address this challenge, we propose UP-DPC: Ultra-Scalable Parallel ...

Sequential Recommendation plays a significant role in daily recommendation systems, such as e-commerce platforms like Amazon and Taobao. However, even with the advent of large models, these platforms often face sparse issues in the historical browsing ...

The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-...

Accurate traffic prediction is one of the most important techniques in building a smart city. Many works, especially deep learning models, have made great progress in traffic prediction based on rich historical data. However, many cities still suffer from ...

Despite the massive surge of evolving social network analysis in popularity, existing research usually represent the observed social interactions among individuals as completely credible edges. However, due to information inaccuracy, individual non-...

Density-based clustering is a fundamental and effective tool for recognizing connectivity structure. The density peak, the data object with the maximum density within a predefined sphere, plays a critical role. However, Density Peak Estimation (...

Spectral Clustering (SC) is an effective clustering method for its excellent performance in partitioning non-linearly distributed data. On the other hand, Ensemble Clustering (EC), a different clustering technology, can promote cluster quality by ...

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods ...

Urban metros have become the foremost public transit to modern cities, carrying millions of daily rides. As travel efficiency matters to the work productivity of the city, shortening passengers’ travel time for metros is therefore a pressing need, ...

• A general and unified framework Robust and Efficient Spectral k-Means (RESKM) is proposed in this work to accelerate the large-scale Spectral Clustering.

Spectral Clustering is an effective preprocessing method in communities for its excellent performance, but its scalability still is a challenge. Many efforts have been made to face this problem, and several solutions are proposed, ...

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually ...

Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative ...