ACM Transactions on the Web

Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a variety of fields, including object tracking, recommender systems, similarity search, and so on. Recent methods for graph ...

This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity ...

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users’ personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical ...

The prosperity of knowledge graphs, as well as related downstream applications, has raised the urgent need for knowledge graph completion techniques that fully support knowledge graph reasoning tasks, especially under the circumstance of training data ...

The task of sequential recommendation aims to predict a user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s ...

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node ...

Graph neural networks have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by the cold-start problem and data ...

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and ...

Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in ...

Modeling the dynamic interactions between users and items on knowledge graphs is crucial for improving the accuracy of recommendation. Although existing methods have made great progress in modeling the dynamic knowledge graphs for recommendation, they ...

When people use web search engines to find information on debated topics, the search results they encounter can influence opinion formation and practical decision-making with potentially far-reaching consequences for the individual and society. However, ...

Usually generated by ordinary users and often not particularly designed for learning, the videos on video-sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing ...

Cross-site user identity linkage (UIL) aims to link the identities of the same person across different social media platforms. Social media practitioners and service providers can construct composite user portraits based on cross-site UIL, which helps ...

Harmful textual content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to this issue is developing detection models that rely on human annotations. However, the tasks required to build ...