IEEE Symposium on Graph Data Mining for Services (GDMS)

Graphs, in a structural data format, are widely present in real-world applications. With their inherent capacity to model complex relationships and dependencies, graphs are increasingly common in the field of service computing. The Internet of Things (IoT), for instance, naturally employs graph topologies, where devices are depicted as nodes connected by edges defining their interactions and data flow. This symposium aims to explore the innovative domain of Graph Data Mining within service-oriented architectures, focusing on extracting valuable knowledge and insights from graph-
based data.

Graph structures are also essential in other various aspects of service computing, like network routing and topology management, communication in distributed systems, and database relationship modelling. These applications highlight the significance of graph mining for services. Analysing and mining graph structures are crucial for enhancing system performance, reliability, and efficiency. This includes graph mining tasks such as managing routing and topology, improving communication protocols in distributed systems, and understanding data relationships.

This symposium will provide a platform for innovations in graph mining for services. We invite contributions that not only on advanced graph mining algorithms but also showcase their practical applications. Our goal is to gather a variety of novel ideas that demonstrate the depth and breadth of graph structures in enhancing service computing applications.

Topics of interest

The symposium encourages submissions on topics that include, but are not limited to:

• Advanced graph theory algorithms for service computing networks;
• Graph-based models for managing complex service computing infrastructures;
• Novel approaches in graph mining for enhancing distributed systems;
• Graph mining for database management;
• Innovative task scheduling and resource allocation models using graph theory;
• Case studies demonstrating the impact of graph mining on service computing performance;
• Graph mining for recommendation services;
• Graph-based security models for network and service integrity;
• Graph mining for the Internet of Things (IoT);
• Applications of graph neural networks in service-oriented architectures;
• Best practices for integrating graph databases in service computing; and
• Techniques for graph visualisation in monitoring and managing service computing systems