A Review on Graph-Theoretic Techniques for Web Log Data

Abstract

The web mining technique is used to analyze pre-existing databases to create new information. The increasing volume and complexity of web log data pose significant challenges in extracting meaningful insights for enhancing web services and user experience. This review paper systematically explores the application of graph theoretic techniques in analyzing web log data, 
aiming to provide a comprehensive understanding of the state-of-the-art methodologies and their effectiveness. The first section of the review introduces the fundamental concepts of web log data and highlights the importance of leveraging graph theory to model intricate relationships within the data. Subsequently, the paper categorizes existing graph-based approaches into different classes based on their primary objectives, such as anomaly detection, user behavior analysis, and community detection. The review scrutinizes each category, delving into specific algorithms, methodologies, and their respective strengths and limitations. Noteworthy techniques include PageRank algorithms for identifying influential pages, community detection algorithms for uncovering hidden structures within user interactions, and anomaly detection methods for identifying irregular patterns indicative of security threats or system malfunctions.
By consolidating the diverse approaches and methodologies employed in the intersection of graph theory and web log data analysis, this review aims to serve as a valuable resource for researchers, practitioners, and academicians seeking a comprehensive understanding of the current landscape and future directions in this burgeoning field. Ultimately, the insights garnered from this review contribute to the advancement of techniques for extracting actionable knowledge from web log data, with implications for web development, user experience enhancement, and cyber security. This paper presents a graph-theoretic- based technique used for the mining of web documents.