# Recent Advances in Efficient Dynamic Graph Processing
## 5 Systems  
Graph processing systems serve as a specialized computational framework for managing and analyzing massive graph-structured data. These platforms are specifically engineered to address the unique demands of processing extremely large graphs, including social networks, web connectivity graphs, and biological interaction networks that typically comprise billions of interconnected nodes and edges. By providing optimized storage and processing capabilities, such systems overcome the significant computational challenges inherent in working with graph data at scale.  
The study in [171] introduces a distributed in-memory graph management system specifically designed for handling large-scale dynamically changing graphs while ensuring efficient query processing. STINGER (Spatio-Temporal Interaction Networks and Graph Extensible Representation) [122] represents an open-source, high-performance data structure specifically designed for dynamic graph analysis. Building on a block-linked list architecture, this framework allows for the gradual expansion of both vertices and edges through incremental block additions. Recognizing STINGER's limitations in distributed environments, DISTINGER [123] is developed as an extension to enable cluster-based graph analytics. This enhanced version implements a master/slave architecture optimized for cluster deployment, incorporating hash-based partitioning and message batching techniques. DISTINGER maintains all native STINGER operations while adding distributed support for vertex and edge modifications across the computing cluster. The EAGr system introduced in [172] provides an efficient solution for processing numerous continuous neighborhood-based aggregate queries on large, rapidly changing graphs.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Problem** & **Algorithm** & **Graph Type** & \begin{tabular}{c} **Update** \\ **Mode** \\ \end{tabular} & **Fully Dynamic** & \begin{tabular}{c} **Incremental** \\ **Dynamic** \\ \end{tabular} & \
\begin{tabular}{c} **Decremental** \\ **Dynamic** \\ \end{tabular} &
\begin{tabular}{c} **Streaming** \\ **Dynamic** \\ \end{tabular} \\ \hline Partitioning & Vaquero [111] & General & Single & ✓ & ✓ & ✓ & ✓ \\ Partitioning & LogGP [117] & General & Single & ✗ & ✗ & ✗ & ✓ \\ Partitioning & Hermes [24] & General & (distributed) & ✗ & ✓ & ✗ & ✗ \\ Partitioning & Leopard [12] & General & Batch & ✓ & ✓ & ✓ & ✗ \\ Partitioning & Li [113] & General & Batch & \(O(|C_{S}|_{i}+|C_{S}|_{i})\cdot\log|CS_{i}|)\) & ✓ & ✓ & ✗ \\ Partitioning & InCDNE [115] & General & Batch & ✗ & ✓ & ✗ & ✗ \\ Partitioning & InCGCGGP [115] & General & Batch & ✗ & ✓ & ✗ & ✗ \\ Clustering & danySCAN [120] & Weighted & Single & ✗ & ✓ & ✗ & ✗ \\ Clustering & BOTBIN [118] & General & Single & \(O(\log n\cdot\log\frac{M+n}{P_{f}})\) & ✓ & ✓ & ✗ \\  & & & Batch & & & \\ Clustering & DPISCAN [119] & General & (distributed & ✗ & \(O(\frac{\log\log n)n+\delta}{P})\) & ✗ & ✗ \\  & & & \& parallel) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of time complexity for dynamic graph separation algorithms.  
Effective workload distribution plays a crucial role in determining the overall performance of distributed graph processing systems. The dynamic nature of evolving graphs often creates significant workload imbalances, as certain vertices may experience substantially higher update frequencies than others during specific time intervals. To address this challenge, [PERSON]. [121] introduces iGraph, a specialized system designed for efficient processing of continuously changing graphs while maintaining balanced workloads. The computational power of GPUs has been widely leveraged to speed up graph processing tasks. Addressing this opportunity, Ref. [124] introduces a GPU-optimized dynamic graph storage architecture designed for seamless integration with existing graph algorithms.