  Graphs data is crucial for many applications, and much of it exists in the
relations described in textual format. As a result, being able to accurately
recall and encode a graph described in earlier text is a basic yet pivotal
ability that LLMs need to demonstrate if they are to perform reasoning tasks
that involve graph-structured information. Human performance at graph recall
has been studied by cognitive scientists for decades, and has been found to
often exhibit certain structural patterns of bias that align with human
handling of social relationships. To date, however, we know little about how
LLMs behave in analogous graph recall tasks: do their recalled graphs also
exhibit certain biased patterns, and if so, how do they compare with humans and
affect other graph reasoning tasks? In this work, we perform the first
systematical study of graph recall by LLMs, investigating the accuracy and
biased microstructures (local structural patterns) in their recall. We find
that LLMs not only underperform often in graph recall, but also tend to favor
more triangles and alternating 2-paths. Moreover, we find that more advanced
LLMs have a striking dependence on the domain that a real-world graph comes
from -- by yielding the best recall accuracy when the graph is narrated in a
language style consistent with its original domain.
