Graph Analytics for Complex Networks – Understanding Social, Financial, and Biological Networks

Abstract

Complex networks are pervasive in social, financial, and biological systems. Traditional analytics often fail to capture intricate relationships and dynamic interactions among entities in these networks. Graph analytics provides a structured approach to analyze such networks by representing nodes and edges and applying computational techniques to extract meaningful patterns. This paper investigates the role of graph analytics in uncovering community structures, detecting anomalies, predicting interactions, and understanding large-scale network dynamics. Case studies across social networks, financial fraud detection, and protein interaction networks illustrate practical applications. Comparative analysis demonstrates the power of graph analytics in uncovering insights that are otherwise hidden in tabular or conventional datasets.

KEYWORDS: Graph Analytics, Complex Networks, Social Network Analysis, Financial Networks, Biological Networks, Network Visualization