GraphRAG is the approach of building a graph from a corpus first, then answering questions against the graph rather than against raw chunks. Microsoft Research published the best-known version, "From Local to Global: A Graph RAG Approach to Query-Focused Summarization".
The pipeline, in outline: extract entities and relationships from every document, build a graph, detect communities of densely connected entities, summarise each community, and answer questions by drawing on the community summaries rather than on retrieved passages.
It targets a specific failure of chunk retrieval: questions about the corpus as a whole. "What are the main themes here" cannot be answered by retrieving the five most similar chunks, because no chunk contains the answer. It has to be assembled from everything.
That class of question is exactly what people want from a second brain and exactly what similarity search is worst at.
Read the pipeline again against a well-maintained wiki and most of it is already built.
Entity extraction is what ingestion does when it writes entity pages. Relationships are the wikilinks, with types if you added them. The graph is the vault. What is missing is the community layer: the detection of clusters and the summaries over them.
And that missing piece has a manual equivalent you are already writing. Synthesis pages are community summaries, produced by a model that read the sources rather than by an algorithm clustering entities.
Probably not, at personal scale, and the honest reason is size. The approach earns its cost on corpora too large to have been curated by hand. A few thousand curated pages with real links do not need automated community detection to find their own themes; you can see the clusters in the graph view.
There is a case for it as an audit rather than as a retrieval system. Running community detection over your exported graph and comparing the algorithmic clusters against your synthesis pages tells you where the vault has themes you have not noticed. That is a once-a-quarter exercise, not infrastructure.
HippoRAG applies personalised PageRank over a knowledge graph for multi-hop questions, which is a more direct analogue of what an agent does walking links outward from a starting page.
Read the papers themselves rather than summaries of them. Both are specific about the conditions their results hold under, and those conditions are what decide whether the approach transfers to your situation.