Benchmark: jsNetworkX neighbors and things

HTML Preparation code:

<script src='https://cdn.rawgit.com/fkling/JSNetworkX/master/jsnetworkx.js'></script>

Script Preparation code:

const graph_size = 1000;
var G = new jsnx.Graph();
for (let i=0; i<graph_size-1; i++) {
	G.addEdge(i, i+1); 
  	if (i % 10 == 0) {
    	G.addEdge(i, 'tens');
    }
}
G.addNode('none');

// directional graph http://jsnetworkx.org/api/#/v/v0.3.4/DiGraph
var diG = new jsnx.DiGraph();
// G.addEdgesFrom([[3,4], [4,5]], {color: 'red'});
diG.addEdgesFrom([
  [100, 201], 
  [100, 202],
  [100, 203],
  [100, 204],
  [100, 205],
  [205, 301], // grand child
  [10,  100], // points from parent of 100
]);
console.log('children of 100: ', diG.successors(100)); // should return [ 201, 202, 203, 204, 205 ]

Tests:

neighbors one
G.neighbors(123);
neighbors many
G.neighbors('tens');
neighbors None
G.neighbors('none');
directed edge (one)
diG.addEdge([2, 3]);
directed weighted edge (one)
diG.addEdge([2, 3], {weight: 4.7});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
neighbors one
neighbors many
neighbors None
directed edge (one)
directed weighted edge (one)

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

I'll break down the benchmark and provide explanations for each test case.

**Overview**

The provided JSON represents a JavaScript microbenchmarking test case, specifically testing the `neighbors()` function of the JSNetworkX library on directed graphs. The tests are designed to measure performance variations in different scenarios.

**Benchmark Definition JSON**

* **"Name": "jsNetworkX neighbors and things"**: This is the name of the benchmark.
* **"Description": "This is very incomplete"**: A brief description of the benchmark, which is currently incomplete.
* **"Script Preparation Code"`: This code initializes a JSNetworkX graph (`G`) with a specific size (1000) and adds edges to it. The `if` statement introduces an additional edge every 10th iteration.
* **"Html Preparation Code": "<script src='https://cdn.rawgit.com/fkling/JSNetworkX/master/jsnetworkx.js'></script>"`: This code includes the JSNetworkX library for the benchmark.

**Individual Test Cases**

Each test case is represented by a JSON object containing:

1. **"Benchmark Definition"`: A specific JavaScript expression that tests the `neighbors()` function.
2. **"Test Name"`: A descriptive name for the test case.

Let's analyze each test case:

### neighbors one

* **"Benchmark Definition": "G.neighbors(123);"`
* **"Test Name": "neighbors one"`

This test case calls the `neighbors()` function on a specific node (123) in the graph. The purpose of this test is to measure the performance of finding neighbors for a single node.

**Pros and Cons:**

Pros:

* Simple and focused test case
* Easy to understand

Cons:

* May not accurately represent real-world scenarios where multiple nodes are involved

### neighbors many

* **"Benchmark Definition": "G.neighbors('tens');"`
* **"Test Name": "neighbors Many"`

This test case calls the `neighbors()` function on a specific node (`'tens'`) in the graph. The purpose of this test is to measure the performance of finding neighbors for multiple nodes.

**Pros and Cons:**

Pros:

* More representative of real-world scenarios where multiple nodes are involved
* Can help identify performance issues with neighbor lookup

Cons:

* May be slower than other test cases due to larger number of neighbors

### neighbors None

* **"Benchmark Definition": "G.neighbors('none');"`
* **"Test Name": "neighbors None"`

This test case calls the `neighbors()` function on a node that does not exist (`'none'`) in the graph. The purpose of this test is to measure the performance of handling non-existent nodes.

**Pros and Cons:**

Pros:

* Helps identify performance issues with handling invalid input

Cons:

* May be slower than other test cases due to unnecessary computations

### directed edge (one)

* **"Benchmark Definition": "diG.addEdge([2, 3]);"`
* **"Test Name": "directed edge (one)"**

This test case adds a single directed edge to the graph.

**Pros and Cons:**

Pros:

* Simple and focused test case
* Easy to understand

Cons:

* May not accurately represent real-world scenarios where multiple edges are involved

### directed weighted edge (one)

* **"Benchmark Definition": "diG.addEdge([2, 3], {weight: 4.7});"`
* **"Test Name": "directed weighted edge (one)"**

This test case adds a single directed weighted edge to the graph.

**Pros and Cons:**

Pros:

* More representative of real-world scenarios where multiple edges are involved
* Can help identify performance issues with weighted edges

Cons:

* May be slower than other test cases due to larger number of edge properties

### neighbors None (Benchmark Result)

The benchmark result for this test case shows that Chrome 84 on Windows has an execution rate of approximately 45,491.57 executions per second.

**Other Alternatives**

To improve the accuracy and reliability of these tests, you may consider:

1. **Increasing graph size**: Increase the size of the graph to better represent real-world scenarios.
2. **Introducing more nodes and edges**: Add more nodes and edges to the graph to simulate more complex scenarios.
3. **Using different edge weights**: Use different edge weights or distributions to simulate weighted edges.
4. **Incorporating randomization**: Incorporate randomness in the test cases to simulate real-world variability.

By considering these alternatives, you can make the benchmark more comprehensive and representative of real-world performance issues with JSNetworkX.

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36

Browser/OS: Chrome 84 on Windows

View result in a separate tab

Test name	Executions per second
neighbors one	401818.2 Ops/sec
neighbors many	44591.6 Ops/sec
neighbors None	1019346.8 Ops/sec
directed edge (one)	208610.6 Ops/sec
directed weighted edge (one)	159425.2 Ops/sec

Related benchmarks:

jsNetworkX neighbors and things (version: 1)

This is very incomplete

Comparing performance of: neighbors one vs neighbors many vs neighbors None vs directed edge (one) vs directed weighted edge (one)

Created: 6 years ago by: Registered User

Jump to the latest result

neighbors one

neighbors many

neighbors None

directed edge (one)

directed weighted edge (one)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):