Topologically Sorting DAGS def naive topsort(G,S=None): if S is None: S=set(G) # Default: All nodes if len(S)==1: return list(S)# Base case, single node v=S.pop() seq=naive topsort(G,S) min i=0 for i,u in enumerate(seq): if v in G[u]: min i=i+1 seq.insert (min i,v) # Reduction: Remove a node # Recursion (assumption), n=1 # After all dependencies return seq Using adjacency sets or dicts to represent a DAG. What's the expected runtime?

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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## Topologically Sorting DAGs

### Python Code for Naive Topological Sort

```python
def naive_topsort(G, S=None):
    if S is None: S = set(G)       # Default: All nodes
    if len(S) == 1: return list(S) # Base case, single node
    v = S.pop()                    # Reduction: Remove a node
    seq = naive_topsort(G, S)      # Recursion (assumption), n=1
    min_i = 0
    for i, u in enumerate(seq):
        if v in G[u]: min_i = i + 1 # After all dependencies
    seq.insert(min_i, v)
    return seq
```

### Explanation

- **Using adjacency sets or dictionaries** to represent a Directed Acyclic Graph (DAG).
- The function checks if it's working with all nodes by default if `S` is `None`.
- It handles the base case where there's only a single node.
- The process involves removing a node and recursively solving the smaller graph.
- Dependencies are checked to ensure correct order by adjusting the insertion index.
- **Expected runtime inquiry**: What’s the expected runtime?

This code snippet defines a recursive approach to perform a naive topological sort on Directed Acyclic Graphs (DAGs) using Python, highlighting the default assumptions, base cases, and steps of recursion and reduction within the function.
Transcribed Image Text:## Topologically Sorting DAGs ### Python Code for Naive Topological Sort ```python def naive_topsort(G, S=None): if S is None: S = set(G) # Default: All nodes if len(S) == 1: return list(S) # Base case, single node v = S.pop() # Reduction: Remove a node seq = naive_topsort(G, S) # Recursion (assumption), n=1 min_i = 0 for i, u in enumerate(seq): if v in G[u]: min_i = i + 1 # After all dependencies seq.insert(min_i, v) return seq ``` ### Explanation - **Using adjacency sets or dictionaries** to represent a Directed Acyclic Graph (DAG). - The function checks if it's working with all nodes by default if `S` is `None`. - It handles the base case where there's only a single node. - The process involves removing a node and recursively solving the smaller graph. - Dependencies are checked to ensure correct order by adjusting the insertion index. - **Expected runtime inquiry**: What’s the expected runtime? This code snippet defines a recursive approach to perform a naive topological sort on Directed Acyclic Graphs (DAGs) using Python, highlighting the default assumptions, base cases, and steps of recursion and reduction within the function.
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