GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting

Belangrijkste concepten

The author presents GVE-Leiden as an optimized parallel implementation of the Leiden algorithm for community detection on shared memory multicore systems, outperforming other implementations significantly.

Samenvatting

The content discusses the optimization of the Leiden algorithm for community detection in shared memory settings. It introduces GVE-Leiden, which achieves high processing rates and improved performance compared to other implementations like the original Leiden, igraph Leiden, and NetworKit Leiden.
Community detection is crucial in various applications with large datasets. The Louvain method may produce disconnected communities, leading to the proposal of the Leiden algorithm by Traag et al. The refinement phase in the Leiden algorithm allows for better identification of well-connected communities.
Optimization techniques are applied to enhance the aggregation phase of the Leiden algorithm. Various strategies such as dynamic loop scheduling and threshold scaling are utilized to improve efficiency. Results show that a greedy approach performs best in terms of runtime and modularity.
The experimental setup includes a server with dual Intel Xeon Gold 6226R processors and graphs sourced from SuiteSparse Matrix Collection. Performance comparisons show that GVE-Leiden outperforms other implementations in terms of runtime, processing rate, and modularity.
Disconnected communities are identified using a parallel algorithm that explores different approaches involving BFS or DFS traversal methods. The evaluation demonstrates the effectiveness of GVE-Leiden in achieving faster processing rates and improved performance compared to existing implementations.

Statistieken

Our Leiden implementation, GVE-Leiden, achieves a processing rate of 403 million edges/s on a 3.8 billion edge graph.
GVE-Leiden outperforms original Leiden by 436×, igraph Leiden by 104×, and NetworKit Leiden by 8.2×.
Modularity obtained with GVE-Leiden is on average 0.3% lower than original Leiden and igraph Leiden but 25% higher than NetworKit Leidens.
Runtime comparison shows that GVE-Leidens finds communities in 9.4 seconds on sk-2005 graph.

Citaten

"No more disconnected communities with GVE-Leidens optimized parallel implementation."
"GVE-Leidens performance scaling is impressive at an average rate of 1.6×."

Belangrijkste Inzichten Gedestilleerd Uit

GVE-Leiden

by Subhajit Sah... om arxiv.org 03-12-2024

https://arxiv.org/pdf/2312.13936.pdf

Diepere vragen

How does the optimization for aggregation phase impact overall community detection results

The optimization for the aggregation phase in GVE-Leiden plays a crucial role in improving the overall community detection results. By efficiently aggregating communities into super-vertices, GVE-Leiden reduces the computational complexity of subsequent passes of the Leiden algorithm. This optimization helps in speeding up the identification of well-connected communities and enhances the scalability of the algorithm on large graphs. Additionally, by utilizing parallel processing techniques during aggregation, GVE-Leiden is able to handle massive datasets more effectively, leading to faster convergence and improved modularity scores.

What implications does identifying disconnected communities have on network analysis

Identifying disconnected communities has significant implications for network analysis. These disconnected components can represent isolated clusters or outliers within a network that may have unique characteristics or play specific roles in network dynamics. Understanding these disconnected communities can provide insights into potential anomalies, structural weaknesses, or distinct subgroups within a network. Analyzing these components can help in identifying influential nodes, detecting hidden patterns, and optimizing strategies for targeted interventions or improvements in various applications such as social networks, biological networks, transportation systems, and communication networks.

How can parallel algorithms like those used in GVE-Leidens be applied to other computational problems

Parallel algorithms like those used in GVE-Leidens can be applied to other computational problems across various domains to enhance performance and scalability. For instance:

Graph Processing: Parallel algorithms can be utilized for graph traversal (BFS/DFS), shortest path calculations (Dijkstra's algorithm), minimum spanning tree construction (Kruskal's/Prim's algorithm), etc.

Machine Learning: Parallel algorithms are beneficial for training complex models such as neural networks using techniques like data parallelism or model parallelism.

Optimization Problems: Parallel algorithms are effective for solving optimization problems like linear programming, integer programming using methods like branch-and-bound or genetic algorithms.

Data Analysis: In data analytics tasks involving clustering (k-means), dimensionality reduction (PCA), regression analysis (linear regression), parallel algorithms improve efficiency when dealing with large datasets.

By leveraging parallel computing capabilities effectively across diverse problem domains similar to how it was done in GVE-Leidens for community detection tasks on shared memory multicore systems will lead to enhanced performance and faster computation times while handling complex computations efficiently at scale.