In a network of nodes, each node i is directly connected to another node j if and only if graph[i][j] = 1.
Some nodes initial are initially infected by malware.  Whenever two nodes are directly connected and at least one of those two nodes is infected by malware, both nodes will be infected by malware.  This spread of malware will continue until no more nodes can be infected in this manner.
Suppose M(initial) is the final number of nodes infected with malware in the entire network, after the spread of malware stops.
We will remove one node from the initial list.  Return the node that if removed, would minimize M(initial).  If multiple nodes could be removed to minimize M(initial), return such a node with the smallest index.
Note that if a node was removed from the initial list of infected nodes, it may still be infected later as a result of the malware spread.
 


Example 1:
Input: graph = [[1,1,0],[1,1,0],[0,0,1]], initial = [0,1]
Output: 0

Example 2:
Input: graph = [[1,0,0],[0,1,0],[0,0,1]], initial = [0,2]
Output: 0

Example 3:
Input: graph = [[1,1,1],[1,1,1],[1,1,1]], initial = [1,2]
Output: 1

 
Note:

1 < graph.length = graph[0].length <= 300
0 <= graph[i][j] == graph[j][i] <= 1
graph[i][i] == 1
1 <= initial.length <= graph.length
0 <= initial[i] < graph.length

```python

USER_FILENAME = "<user_code>"
import sys, json, builtins
import math, heapq, bisect, itertools, functools, collections, statistics, fractions, decimal, operator, array, random, re, string, os, pathlib
try:
    import resource as _res
except Exception:
    _res = None
from collections import Counter, defaultdict, deque, OrderedDict
from functools import lru_cache, reduce
from itertools import accumulate, permutations, combinations, product
from math import gcd, ceil, floor, sqrt
from decimal import Decimal
try:
    from typing import *
except Exception:
    pass
try:
    sys.setrecursionlimit(1_000_000)
except Exception:
    pass
try:
    import site as _site
    try:
        _sys_sites = _site.getsitepackages() if hasattr(_site, "getsitepackages") else []
        for _p in _sys_sites:
            if _p and _p not in sys.path:
                sys.path.append(_p)
    except Exception:
        pass
    try:
        _usp = _site.getusersitepackages()
        if isinstance(_usp, str):
            _usp = [_usp]
        for _p in _usp:
            if _p and _p not in sys.path:
                sys.path.append(_p)
    except Exception:
        pass
except Exception:
    pass

def _disabled_input(*args, **kwargs):
    raise RuntimeError("input() is disabled in callable mode")
builtins.input = _disabled_input

ns = {"__name__": "__judge__"}  # prevent if __name__ == "__main__" blocks
for _k, _v in list(globals().items()):
    if not _k.startswith("_"):
        ns[_k] = _v
_CODE = "class Solution(object):\n    def minMalwareSpread(self, graph, initial):\n        # 1. Color each component.\n        # colors[node] = the color of this node.\n\n        N = len(graph)\n        colors = {}\n        c = 0\n\n        def dfs(node, color):\n            colors[node] = color\n            for nei, adj in enumerate(graph[node]):\n                if adj and nei not in colors:\n                    dfs(nei, color)\n\n        for node in range(N):\n            if node not in colors:\n                dfs(node, c)\n                c += 1\n\n        # 2. Size of each color.\n        # size[color] = number of occurrences of this color.\n        size = collections.Counter(list(colors.values()))\n\n        # 3. Find unique colors.\n        color_count = collections.Counter()\n        for node in initial:\n            color_count[colors[node]] += 1\n\n        # 4. Answer\n        ans = float('inf')\n        for x in initial:\n            c = colors[x]\n            if color_count[c] == 1:\n                if ans == float('inf'):\n                    ans = x\n                elif size[c] > size[colors[ans]]:\n                    ans = x\n                elif size[c] == size[colors[ans]] and x < ans:\n                    ans = x\n\n        return ans if ans < float('inf') else min(initial)\n    \n    \n# Algorithm\n\n# This algorithm has a few parts:\n\n# Coloring each component: For each node, if it isn't yet colored, use a depth-first search to traverse its component, coloring that component with a new color.\n\n# Size of each color: Count the number of occurrences of each color.\n\n# Find unique colors: Look at the colors of nodes in initial to see which nodes have unique colors.\n\n# Choose answer: For each node with a unique color, find the size of that color. The largest size is selected, with ties broken by lowest node number.\n\n# If there is no node with a unique color, the answer is min(initial).\n"
_co = compile(_CODE, USER_FILENAME, "exec")
exec(_co, ns, ns)

_target = None
_fn_name = 'minMalwareSpread'
if _fn_name:
    if "Solution" in ns:
        try:
            _obj = ns["Solution"]()
            if hasattr(_obj, _fn_name):
                _target = getattr(_obj, _fn_name)
        except Exception:
            _target = None
    if _target is None and _fn_name in ns and callable(ns[_fn_name]):
        _target = ns[_fn_name]
# Fallback: if there's exactly one user-defined function, use it
if _target is None:
    _cands = [v for k, v in ns.items() if callable(v) and getattr(v, "__module__", "") in ("__judge__", "__main__")]
    _user_funcs = [c for c in _cands if getattr(c, "__name__", "").startswith("_") is False]
    if len(_user_funcs) == 1:
        _target = _user_funcs[0]
if _target is None:
    raise RuntimeError(f"Could not resolve callable '{_fn_name}'")

_raw = sys.stdin.read().strip()
if _raw:
    _parsed = json.loads(_raw)
    if isinstance(_parsed, dict):
        _kwargs = _parsed.get("kwargs", {})
        _args = _parsed.get("args", [])
    else:
        _args, _kwargs = _parsed, {}
else:
    _args, _kwargs = [], {}
if not isinstance(_args, (list, tuple)):
    _args = [_args]
_res = _target(*_args, **_kwargs)
try:
    sys.stdout.write(repr(_res) + "\n")
except Exception:
    pass

```

Measured memory_bytes: 11008