Introduction to the Special Issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications: Part 1

Abstract

We are delighted to present this special issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications. Graph mining plays an important role in data mining on the Web. It can take full advantage of the growing and easily accessible big data resources on the Web, such as rich semantic information in social media and complex associations between users in online social networks, which is crucial for the development of systems and applications such as event detection, social bot detection, and intelligent recommendation. However, extracting valuable and representative information from Web graph data is still a great challenge and requires research and development on advanced techniques. The purpose of this special issue is to provide a forum for researchers and practitioners to present their latest research findings and engineering experiences in the theoretical foundations, empirical studies, and novel applications of Graph Mining. This special issue consists of two parts. In Part 1, the guest editors selected 10 contributions that cover varying topics within this theme, ranging from reinforced and self-supervised GNN architecture search framework to the streaming growth algorithm of bipartite graphs. Yang et al. in “RoSGAS: Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search” proposed a novel Reinforced and Self-supervised GNN Architecture Search framework named RoSGAS, which gains improvement in terms of accuracy, training efficiency, and stability. And has better generalization when handling unseen samples. Du et al. in “Niffler: Real-time Device-level Anomalies Detection in Smart Home” proposed a novel notion—a correlated graph, and with the aid of that, they developed a system to detect misbehaving devices without modifying the existing system, which is crucial for the device-level security in the smart home system. And then they further proposed a linkage path model and a sensitivity ranking method to assist in detecting the abnormalities. Sun et al. in “GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group Alignment” presented a novel GroupAligner, a deep reinforcement learning with domain adaptation for social group alignment, which solves the problems of feature inconsistency across different social networks and group discovery within a social network in social group alignment. Zhu et al. in “A Multi-task Graph Neural Network with Variational Graph Auto-encoders for Session-based Travel Packages Recommendation” proposed a novel session-based model named STR-VGAE, which provides robust attributes’ representations and takes the effects of historical sessions for the current session into consideration. The model obtained promising results in the session-based recommendation, and can fill subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously.