Neo4j Graph Database Fundamentals · Pelajaran

Menjalankan Algoritma GDS

Pelajari cara menjalankan berbagai algoritma graf dari pustaka GDS, termasuk memuat graf ke memori dan mengonfigurasi algoritma.

Menjalankan Algoritma GDS adalah pelajaran Neo4j Graph Database Fundamentals gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Neo4j Graph Database Fundamentals, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Neo4j Graph Database Fundamentals mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

GDS Algorithm Workflow

The Graph Data Science (GDS) library in Neo4j helps us run powerful graph algorithms. To use GDS, you generally follow a three-step process:

Project: Load a subset of your graph into GDS's optimized in-memory store.
Execute: Run an algorithm on this in-memory graph.
Write Back: Optionally, write the results back to your Neo4j database.

Projecting Your Graph

Before running an algorithm, GDS needs to know which part of your database graph to analyze. This process is called graph projection.

You define which nodes and relationships, based on their labels and types, should be copied into GDS's fast in-memory representation. This makes algorithm execution much quicker.

Projecting Nodes Only

Let's project a simple graph containing only nodes with the label Person. We give our projected graph a name, like myGraph.

The gds.graph.project procedure creates this in-memory graph.

CALL gds.graph.project(
  'myGraph',
  'Person',
  {}
) YIELD graphName, nodeCount, relationshipCount;

Projecting Nodes and Relationships

More commonly, you'll project both nodes and relationships. Here, we project Person nodes and their KNOWS relationships.

We specify the node label and the relationship type. You can also add properties to the projection.

CALL gds.graph.project(
  'socialGraph',
  'Person',
  'KNOWS'
) YIELD graphName, nodeCount, relationshipCount;

Executing GDS Algorithms

Once your graph is projected, you can run various algorithms. GDS algorithms often have different execution modes:

Stream: Returns results directly as a stream of rows. Great for immediate analysis.
Stats: Returns only summary statistics about the algorithm's run.
Write: Writes the results back to your Neo4j database as new properties or relationships.

PageRank Stream Example

Let's run the PageRank algorithm on our socialGraph in stream mode. PageRank helps identify influential nodes.

We use gds.pageRank.stream and pass the name of our projected graph.

CALL gds.pageRank.stream('socialGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS person, score
ORDER BY score DESC
LIMIT 5;

Customizing Algorithm Settings

GDS algorithms are highly configurable. You can pass a map of parameters to fine-tune their behavior.

Common parameters include maxIterations, dampingFactor (for PageRank), or concurrency. Always check the GDS documentation for specific algorithm parameters.

PageRank with Custom Settings

Here, we run PageRank with a custom dampingFactor and limit the maxIterations. This gives you more control over the algorithm's execution and convergence.

CALL gds.pageRank.stream(
  'socialGraph',
  {
    maxIterations: 10,
    dampingFactor: 0.8
  }
)
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS person, score
ORDER BY score DESC
LIMIT 5;

Persisting Algorithm Results

Often, you'll want to store the results of an algorithm back into your Neo4j database. This allows you to query them later or use them in further analysis.

The write mode of GDS algorithms adds new properties to nodes or creates new relationships based on the algorithm's output.

Writing PageRank Scores

To write results, use the .write procedure, like gds.pageRank.write. You need to specify the property name where the score will be stored on the original nodes.

After running this, each Person node in your database will have a new property, pageRankScore.

CALL gds.pageRank.write(
  'socialGraph',
  {
    writeProperty: 'pageRankScore'
  }
)
YIELD nodeCount, ranIterations, didConverge;

GDS Workflow Check

Which of the following are essential steps in the typical GDS algorithm workflow?

Running GDS Algorithms Recap

Great job! You've learned the core steps for running GDS algorithms:

Projecting your graph into GDS memory using gds.graph.project.
Executing algorithms in stream, stats, or write modes.
Configuring algorithms with custom parameters.
Writing back results to your Neo4j database for persistence.

Next, you'll explore advanced GDS pipelines and integration with machine learning!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Menjalankan Algoritma GDS” gratis?

Ya — teks lengkap “Menjalankan Algoritma GDS” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Neo4j Graph Database Fundamentals, upgrade ke CoddyKit PRO. Kursus Neo4j Graph Database Fundamentals mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Menjalankan Algoritma GDS”?

Pelajari cara menjalankan berbagai algoritma graf dari pustaka GDS, termasuk memuat graf ke memori dan mengonfigurasi algoritma. Kamu berlatih Neo4j Graph Database Fundamentals dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai Neo4j Graph Database Fundamentals?

Tidak diperlukan pengalaman sebelumnya. Neo4j Graph Database Fundamentals di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Menjalankan Algoritma GDS” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran Neo4j Graph Database Fundamentals ini?

Ya. Setiap pelajaran Neo4j Graph Database Fundamentals menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

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