Social Network Analysis (SNA) is a scientific methodology pertaining to social sciences that investigates social structures through the use of networks and graph theory. In essence, it examines how relationships between social entities influence actions and behaviors. The main units of interest are the components and structures within a network, effectively shedding light on the critical aspects of connectivity, social interactions and information flow.

There are certain core features outlined in Social Network Analysis, including:

• Actors and Relationships: Participants in social network generally referred to as 'actors' may comprise individuals, organizations or generally entities. The 'relationships' might be friendships, business partnerships, trade associations or information flows.

• Network Structure Insight: SNA grants understanding of social structures and systems by revealing patterns or characteristics of these networks.

• Visualization: Through SNA, it offers network visualization to ensure better understanding of complex systems for easier analysis.

• Quantitative Analytical Methods: SNA employs quantitative indicators like network diameter, density, centrality, clustering coefficient, among others for comprehensive understanding.

• Network Interactions: SNA offers insight into the modes and manners of interaction within a given network, giving an idea about exchanges, relationships and the interplay of actors.

Crafting an effective SNA strategy is needed for optimized benefits. This entails detailed preliminary investigation, identification of network actors and relationships, and meticulous selection of analytical techniques and targets. In order to comprehend the dynamics of the network, constant monitoring of the network is essential post implementation. Patience, scrupulousness and objectivity are necessary elements of a successful Social Network Analysis, granting it the ability to become a key tool in understanding and interpreting the world around us.

SNA, as a tool, offers several advantages in varied fields such as Sociology, Anthropology, Computer Science, Communication studies, Business and Political Science:

• Unveiling Hidden Patterns: The comprehensive approach used in SNA helps in revealing hidden network patterns behind collective social behaviors or interactions.

• Robust Decision Making: Tactical decisions based on SNA can help in areas such as marketing, design of organizational structure or even in formation of policy decisions, making decision making process more robust and accurate.

• Predictive Insights: Using SNA, it's easier to predict future outcomes based on the connection dynamics, role of key influencers and information flow.

• Interdisciplinary Approach: As SNA is not bound to a specific field, it can be employed in a range of scientific disciplines and contexts.

• Enhanced Understanding: The powerful visualization tools used in SNA enhance the understanding of complex organizations and networks.

However, it's essential to comprehend the potential limitations of SNA:

• Data Privacy and Protection: As SNA entails working with sensitive personal data, there can be potential issues related to privacy, consent and data protection.

• Time and Resources: Collection of network data can be a time-consuming process that requires significant resources.

• Complexity: Depending on network size and structure, SNA can become complex, requiring a high level of expertise for a valid interpretation of the results.

• Partial Network Analysis: There could be a potential risk of making an interpretation based upon an incomplete view of the network, particularly in case of larger complex networks.

• Static Analysis: Networks are dynamic by nature and may change over time, an aspect which SNA, in its traditional form does not take into account.