Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

Data sparsity is one of the most challenging problems for recommender systems. One
promising solution to this problem is cross-domain recommendation, ie, leveraging …

The increasing popularity and diversity of social media sites has encouraged more and
more people to participate on multiple online social networks to enjoy their services. Each …

Cross-platform account matching plays a significant role in social network analytics, and is
beneficial for a wide range of applications. However, existing methods either heavily rely on …

The typical aim of User Identity Linkage (UIL) is to detect when users from across different
social platforms are actually one and the same individual. Existing efforts to address this …

Linking entities from different sources is a fundamental task in building open knowledge
graphs. Despite much research conducted in related fields, the challenges of linkinglarge …

Big data technology has had a significant impact on new business and financial services: for
example, GPS and Bluetooth inspire location-based services, and search and web …