Speeding up Python igraph: cache the membership list outside loops to avoid costly copies

Learn a common igraph performance pitfall in Python: membership property returns a copy per call. Cache it once outside loops to cut list-copy overhead.

When working with igraph, it’s easy to stumble into performance pitfalls that don’t look suspicious at first glance. One such case is accessing a property that returns a fresh list on every call inside a tight loop. If cProfile points at a list comprehension as the hotspot, it might be doing far more work than it appears.

The problem

The following pattern was identified as a bottleneck while iterating over vertices of a Graph:

vals = [ grp.membership[v.index] if v.index < len(grp.membership) else -1 for v in net.vs ]

Here, net is a Graph and grp provides a membership list. The code looks straightforward, but it hides an expensive detail.

What’s really happening

The membership property returns a copy of the underlying list each time it’s accessed. That means the comprehension above is not just reading a list repeatedly; it’s building a full new list for every vertex iteration. If you have many vertices, that becomes costly very quickly.

The copy is deliberate. The intent is to prevent modifications to the internal membership list that could desynchronize it from other cached properties of the same object, like the modularity. In other words, the copy is a safety boundary.

The fix

If performance matters, fetch the membership list once before the loop and reuse that reference throughout the computation. This preserves the safety guarantee while avoiding repeated copying:

members = grp.membership
vals = [ members[v.index] if v.index < len(members) else -1 for v in net.vs ]

Another option, if you explicitly choose to bypass that safety layer, is to use the private attribute directly:

members = grp._membership
vals = [ members[v.index] if v.index < len(members) else -1 for v in net.vs ]

Some teams prefer not to access private attributes. In that case, the first approach—reading the property once and keeping the local reference—is a clean and robust compromise that keeps behavior intact and performance predictable.

Why this matters

Property access that returns copies is easy to overlook. In a hot path, repeatedly calling such a property inside a loop scales poorly and can dominate runtime. Understanding that boundary between public, defensive APIs and their internal representations helps you write code that is both safe and fast. In igraph, this specific boundary exists to prevent accidental mutation that could invalidate other cached values, such as modularity, so treating the returned data as read-only or caching it locally is a practical habit.

Takeaways

If a property returns a list, confirm whether it returns a new copy each time. When it does, cache the result outside the loop. If you absolutely must squeeze out extra performance and your usage is read-only, directly referencing the private attribute is possible, but it trades off encapsulation for speed. A simple refactor to fetch the membership data once usually provides the desired performance without touching internals.