bipartite graph
A graph with two kinds of node, where every relationship joins one kind to the other and never two of the same kind.
Also written: bipartite graphs, bipartite
Learn more in Projecting bipartite and multipartite graphs
Example
Customers and products form a bipartite graph when every [:BOUGHT] relationship runs from a (:Customer) to a (:Product), and no relationship joins two customers.
Adding a single (:Customer)-[:FOLLOWS]->(:Customer) relationship ends the bipartition.
Lessons that use this term
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27 lessons use this term. Results are ordered by term density.
Get started with Graph Data Science104 mentions
- Practice bipartite projectionsGDS basic conceptswhen to preserve labels in your projections, it's time to practice creating bipartite28 mentions
- Projecting bipartite and multipartite graphsGDS basic conceptsalso learned that some algorithms, like PageRank, produce poor results on bipartite structures because the structure itself traps their22 mentions
- Projecting monopartite graphsGDS basic conceptsis what's known as a bipartite graph—a graph whose nodes can be divided into two distinct, non-overlapping sets where connections only occur between16 mentions
- Challenge: Aggregated projection and analysisEssential projection techniquesof movies each director-actor pair has worked on together Project as a bipartite graph (preserving both Director and Actor labels) Make relationships3 mentions
- Practice monopartite projectionsGDS basic conceptswith the Movies dataset to create various monopartite projections—transforming bipartite and multipartite structures into single-type networks that reveal different patterns within the same11 mentions
- Challenge: Multipartite projectionsGDS basic conceptstask: Create a bipartite projection of Users and Genres that preserves their labels and connects them through the movies they2 mentions
- Challenge: Weighted graph projection and analysisWorking with algorithmsSimilarity further by using it in stream mode on a directed, weighted, bipartite graph to analyze user similarity without writing back to the4 mentions
- Module recap and what's nextGDS basic conceptsMonopartite - Same node type (actors ↔ actors) Bipartite - Different node types preserved (users ↔ movies) Multipartite - Multiple types and2 mentions
- Projection modeling for algorithmsEssential projection techniquesdiscovered that the same data can be projected in different ways—monopartite, bipartite, or3 mentions
- Practice relationship aggregationEssential projection techniquesan actor-director bipartite graph where the weight represents how many movies an actor appeared in that were directed by each2 mentions
- Configuring projections with unweighted relationshipsWorking with algorithmsthis lesson, you'll create a user-movie bipartite network using rating data and run Leiden community detection with and without relationship2 mentions
- List and drop graphsGDS basic conceptsthat you've practiced creating various types of projections—monopartite, bipartite, and multipartite—it's time to learn how to manage them2 mentions
- Challenge: Projection modeling and analysisEssential projection techniquesShould this be monopartite (Director -> Director) or bipartite (Director ->1 mention
- Project a graphGDS basic conceptson monopartite graphs (single node type), while others are designed for bipartite graphs (two distinct node1 mention
- Module recapEssential projection techniquesin the same circles?" Choose node types: Monopartite (Actor -> Actor) or Bipartite (User -> Movie) Determine relationships: Direct, inferred, weighted, or aggregated Select the algorithm:1 mention
- Algorithms overviewWorking with algorithmsdegree centrality and PageRank on monopartite graphs, and node similarity on bipartite graphs2 mentions
- Running algorithmsWorking with algorithmsthe bipartite graph looks like1 mention
- Understand the five execution modesWorking with algorithmsand gds.pageRank.stream() to view results directly in query output In the bipartite lessons, you used write mode: gds.nodeSimilarity.write() to persist SIMILAR relationships back to your1 mention
Graph Data Science in Practice36 mentions
- Graph Projections and StructureGDS Foundationsbetween graph structure and node labels in GDS Identify monopartite, bipartite, multipartite, and heterogeneous graph structures Choose appropriate projection strategies based on your target19 mentions
- Projection PracticeGDS Foundationstransformations — connecting nodes through shared neighbours Labelled bipartite projections — preserving two-partition11 mentions
- Algorithm CategoriesGDS FoundationsLeiden needs undirected, Pathfinding needs weighted, Node Similarity for bipartite graphs2 mentions
- Applying AlgorithmsGDS Foundationsgraph projection with user and movie nodes connected by2 mentions
- Fraud DetectionCommunity Detection for Fraud(UserP2P → UserP2P) | Direct transactions | Shared infrastructure | | Bipartite (UserP2P → Card/Device) | Shared infrastructure | Direct transactions | | Heterogeneous (All nodes) | Everything1 mention
- Degree Centrality & WCCCommunity Detection for Fraudcreates a bipartite graph: Actor nodes connected to Movie1 mention
Aura Graph Analytics fundamentals3 mentions
- Aura Graph Analytics workflowAura Graph Analytics in the Aura Workspacegives you a bipartite1 mention
- The AGA workflow end to endFrom the Python clientis a bipartite Actor-ACTED_IN-Movie graph — a direct copy of that slice of1 mention
- Train and publish machine learning modelsAura Graph Analytics in the Aura Workspacethe session created, project a bipartite User-RATED-Movie graph into1 mention