Abstract:

In the burgeoning landscape of digital data, the exponential growth of information across various domains has spurred a pressing need for innovative methods to effectively harness and make sense of this data. Amidst this the significance of graph embedding methods has emerged as a important part in the realm of artificial intelligence (AI) and machine learning (ML) research. Graphs, as abstract mathematical structures representing relationships between entities, offer a powerful framework for modeling complex systems, networks and relations in it. They serve as a natural abstraction for diverse data types, including social networks, citation networks, molecular structures, and recommendation systems, among others. However, while graphs provide a rich representation of interconnected data, their inherent complexity poses significant challenges for conventional algorithms, particularly in tasks such as node classification, link prediction, and graph clustering.

In essence, research in graph embedding methods is indispensable for unlocking the full potential of graph-structured data across a myriad of domains. As the volume and complexity of digital data continue to escalate, the development of robust and scalable embedding techniques becomes required. By bridging the gap between raw graph data and actionable knowledge, graph embedding methods pave the way for a new era of intelligent systems capable of understanding, reasoning, and learning from interconnected data at scale.