SocialMind: Retrieval-Augmented Generation for Influence Graphs

SocialMind is a retrieval-augmented reasoning system that integrates structured knowledge graphs with generative AI to enhance understanding of public figures, social events, and relational semantics. Unlike traditional keyword-based retrieval methods that merely return matching documents, SocialMind combines graph-structured representations with a semantic generation engine—enabling deeper comprehension of queries through the contextualization of interpersonal dynamics, latent associations, and historical trajectories.
‍
With advanced multi-hop retrieval planning and relationship expansion capabilities, users can ask open-ended, high-level questions like “Who might be influencing this decision behind the scenes?” and receive structured, generation-based responses grounded in factual graph data. This opens up new frontiers for social semantics understanding, risk interpretation, and contextual insight generation.

Technical Overview

Relational Graph Construction

Constructs semantically rich graph nodes and edges representing individuals, statements, events, and interactions—anchored along a temporal axis.

Maintains continuous updates via the PulseGraph Engine to accurately reflect real-time relational dynamics and evolving public discourse.

Intelligent Multi-Hop Retrieval

Processes natural language inputs and autonomously formulates multi-step query paths traversing relevant individuals and event nodes.

Supports complex logical queries encompassing inferred relationships, contextual expansion, and event sequencing.

Deep Semantic Generation

Beyond direct retrieval, generates structured outputs including influence maps, contextual explanations, and risk assessments.

Ensures outputs are graph-grounded and visualizable, providing actionable insights for strategic monitoring and decision support.