Create an opensource social graph tool identifying relationships between actors, particularly threat actors, creating graphs and clusters. Identify the strength of relationships Actor-to-Actor or Actor-to-Cluster.
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Chat messages will be run through an embedder or sentence transformer to create vectorizations and will be saved in a vector index.
LLMs grounded by knowledge graphs (graph-rag) will be used to answer questions such as "Does Alice know Bob?"
and "What does Alice know about Bob?"
Like the jibble project, we will use IRC bots to monitor chat rooms and infer relationships between actors. We will use the following methods to infer relationships:
LLMs can significantly enhance the inference process by understanding the context, sentiment, and the subtleties of human conversation beyond the capabilities of traditional rule-based systems.
Scheduled Summarization: Periodically, the system can invoke an LLM to summarize recent conversations. This not only helps in understanding the general discourse but can also highlight recurring themes or subjects around which relationships may form.
Trigger-based Collection: Implement triggers based on conversational cues, such as pauses in dialogue or shifts in topics, to capture and analyze snapshots of conversations. This method ensures that the analysis is contextually relevant and timely.
Interval-based Analysis: Similar to windowing, this involves examining conversations within set intervals to infer relationships and summarize content. This approach can be dynamically adjusted based on the volume of conversation or specific events within the chat room.
Analyze conversation to find conversation leaders or the perceived leader, for many different contexts (primary chat, or within a cluster)
If we are able to infer the strength of relationships, we can use a temporal decay model to reduce the strength of relationships over time.
TBD