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Coverage Centrality is an important metric to evaluate vertex importance in road networks. However, current solutions have to compute the coverage centrality of all the vertices together, which is resource-wasting, especially when only some ...

Answering complex queries with First-order logical operators over knowledge graphs, such as conjunction ($\wedge$), disjunction ($\vee$), and negation ($\neg$) is immensely useful for identifying missing knowledge. Recently, neural symbolic reasoning methods have ...${\mathbb{}}^{}$

In the context of searching a single data graph G, graph pattern matching is to find all the occurrences of a pattern graph Q in G, specified by a matching rule. It is of paramount importance in many real applications such as social network ...

Images play a vital role in social media platforms, which can more vividly reflect people’s inner emotions and preferences, so visual sentiment analysis has become an important research topic. In this paper, we propose a Supervised Contrastive ...

Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance under heterophily scenarios. Most current solutions deal with ...

Inductive relation prediction is an important learning task for knowledge graph reasoning that aims to infer new facts from existing ones. Previous graph neural networks (GNNs) based methods have demonstrated great success in inductive relation ...

Heterogeneous information networks have been proven to effectively improve recommendations due to their diverse information content. However, there are still two challenges for recommendation methods based on heterogeneous information networks. To ...

In tackling Tuberculosis (TB), a critical global health challenge, the integration of Foundation Models (FMs) into diagnostic processes represents a significant advance. FMs, with their extensive pre-training on diverse datasets, hold the promise ...

Group recommendation aims to suggest desired items for a group of users. Existing methods can achieve inspiring results in predicting the group preferences in data-rich groups. However, they could be ineffective in supporting cold-start groups due ...

Recommending medications with electronic health records (EHRs) is a challenging task for data-driven clinical decision support systems. Most existing models learnt representations for medical concepts based on EHRs and make recommendations with ...

Humanity needs to deliberate effectively at scale about highly complex and contentious problems. Current online deliberation tools—such as email, chatrooms, and forums—are however plagued by levels of discussion toxicity that deeply undercut the ...

Deep neural networks (DNN) exhibit powerful feature extraction capabilities, making them highly advantageous in numerous tasks. DNN-based techniques have become widely adopted in the field of speaker recognition. However, imperceptible adversarial ...