[BUG] Running clustering app on graphscope.nx is slower than networkx over p2p dataset

Descrizione

  import os
  import graphscope.nx as gs_nx
  import networkx as nx
  import time


  start = time.time()
  g1 = nx.read_edgelist(
      os.path.expandvars('./p2p-31.e'),
      nodetype=int,
      data=False,
      create_using=nx.Graph
  )
  print(type(g1))
  print("networkx = ", time.time() - start)
  # networkx.classes.graph.Graph
  start = time.time()
  g2 = gs_nx.read_edgelist(
      os.path.expandvars('./p2p-31.e'),
      nodetype=int,
      data=False,
      create_using=gs_nx.Graph
  )
  print(type(g2))
  print("gs = ", time.time() - start)
  start = time.time()
  ret_nx = nx.clustering(g1)
  print("networkx = ", time.time() - start)
  # 0.91s

  start = time.time()
  ret_gs = gs_nx.clustering(g2)
  print("gs = ", time.time() - start)
  # 2.12s

  # compare the results
  print(ret_gs == ret_nx)

Guida contributor

Tech stack

python

Dominio

backendperformance

Tipo issue

bug

DifficoltàQuanto dovrebbe essere impegnativa per un nuovo contributor.

3

Tempo stimatoTempo stimato per completare e verificare uno scope piccolo.

half day

Stato attivitàQuanto è probabile che la issue sia ancora attiva e reviewable.

stale

ChiarezzaQuanto chiaramente la issue descrive problema, scope e risultato atteso.

clear

Prerequisiti

Python

Adatta ai principiantiQuanto la issue è adatta a contributor alla prima esperienza.

30

Direzione di ricerca

The issue provides a reproducible script comparing GraphScope.nx and NetworkX clustering on a p2p dataset. The contributor should run the script to confirm the performance gap, then profile GraphScope's clustering implementation to identify bottlenecks. Focus on the C++ backend code in the clustering algorithm (likely in `analytical engine` directories). Check for recent changes or configuration differences that could explain the deviation from the 25x speedup claimed in the blog. The lack of comments or assigned maintainers suggests the issue is open for exploration.