IC-858The choice of graph encoding method significantly changes LLM accuracy on graph reasoning tasks, with incident encoding outperforming adjacency by up to 34 percentage points on connected nodes
The paper measures how nine different textual encodings of the same graph (adjacency, incident, friendship, co-authorship, South Park, Game of Thrones, social network, politician, expert) affect LLM performance on six basic graph tasks. On PaLM 62B with zero-shot prompting, connected nodes accuracy ranges from 4.0% (friendship) to 53.8% (incident), while node degree ranges from 9.0% (SP) to 25.0% (incident). The application-style question encoder also outperforms the graph-style encoder (e.g., 60.8% vs 42.8% on zero-shot edge existence for PaLM 2-xxs). The overall improvement from choosing the best over worst encoding spans 4.8% to 61.8% depending on the task.