Community Discovery algorithms

CDlib collects implementations of several Community Discovery algorithms.

To maintain the library organization as clean and resilient to changes as possible the exposed algorithms are grouped following a simple rationale:

1. Algorithms designed for static networks, and
2. Algorithms designed for dynamic networks.

Moreover, within each category, CDlib groups together approaches sharing a same set of high-level characteristics.

In particular, static algorithms are organized into:

• Those searching for a crisp partition of the node set;
• Those searching for an overlapping clustering of the node set;
• Those that search for a fuzzy partition of the node set;
• Those that cluster edges;
• Those that are designed to partition bipartite networks;
• Those that are designed to cluster feature-rich (node attributed) networks;
• Those that search for antichains in DAG (directed acyclic graphs).

Dynamic algorithms, conversely, are organized to resemble the taxonomy proposed in [Rossetti18]

• Instant Optimal,
• Temporal Trade-off

This documentation follows the same rationale.

• Static Community Discovery
• Dynamic Community Discovery

If you need a summary on the available algorithms and their properties (accepted graph types, community characteristics, computational complexity) refer to:

• Algorithms’ Table

[Rossetti18]

Rossetti, Giulio, and Rémy Cazabet. “Community discovery in dynamic networks: a survey.” ACM Computing Surveys (CSUR) 51.2 (2018): 1-37.