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 …

Real-world information networks are increasingly occurring across various disciplines
including online social networks and citation networks. These network data are generally …

The widespread use of positioning devices has given rise to many trajectories, with each
having three explicit attributes: user ID, location ID, and time-stamp and an implicit attribute …

The development of mobile devices and positioning technology has facilitated the rapid
growth of location-based social networks (LBSNs). Users in these networks can share geo …

It is well known that the number of users is increasing rapidly in online social networks.
People are linked through various types of social relationships. Detecting the type of social …

Linking authors of short-text contents has important usages in many applications, including
Named Entity Recognition (NER) and human community detection. However, certain …

Unsupervised graph representation learning, ie, learning node or graph embeddings from
graph data in an unsupervised manner, has become an important problem when we study …

Graph vertex embeddings based on random walks have become increasingly influential in
recent years, showing good performance in several tasks as they efficiently transform a …

摘要随着智能时代和大数据时代的到来, 各种复杂异构数据不断涌现, 成为数据驱动的人工智能
方法, 机器学习模型的基础. 复杂异构数据的表征直接关系着后续模型的学习性能 …

Graph-based data present unique challenges and opportunities for machine learning. Graph
Neural Networks (GNNs), and especially those algorithms that capture graph topology …