arXiv subjects

Subhajit Sahu

Publications and source records attributed to Subhajit Sahu.

24 records · Page 2Linked to original sources

arXiv2024

DF* PageRank: Improved Incrementally Expanding Approaches for Updating PageRank on Dynamic Graphs

PageRank is a widely used centrality measure that assesses the significance of vertices in a graph by considering their connections and the importance of those connections. Efficiently updating PageRank on dynamic graphs is essential for various applications due to the increasing scale of datasets. This technical report introduces our improved Dynamic Frontier (DF) and Dynamic Frontier with Pruning (DF-P) approaches. Given a batch update comprising edge insertions and deletions, these approaches iteratively identify vertices likely to change their ranks with minimal overhead. On a server featuring a 64-core AMD EPYC-7742 processor, our approaches outperform Static and Dynamic Traversal PageRank by 5.2x/15.2x and 1.3x/3.5x respectively - on real-world dynamic graphs, and by 7.2x/9.6x and 4.0x/5.6x on large static graphs with random batch updates. Furthermore, our approaches improve performance at a rate of 1.8x/1.7x for every doubling of threads.

cs.DC↗

arXiv2024

An Incrementally Expanding Approach for Updating PageRank on Dynamic Graphs

PageRank is a popular centrality metric that assigns importance to the vertices of a graph based on its neighbors and their score. Efficient parallel algorithms for updating PageRank on dynamic graphs is crucial for various applications, especially as dataset sizes have reached substantial scales. This technical report presents our Dynamic Frontier approach. Given a batch update of edge deletion and insertions, it progressively identifies affected vertices that are likely to change their ranks with minimal overhead. On a server equipped with a 64-core AMD EPYC-7742 processor, our Dynamic Frontier PageRank outperforms Static, Naive-dynamic, and Dynamic Traversal PageRank by 7.8x, 2.9x, and 3.9x respectively - on uniformly random batch updates of size 10^-7 |E| to 10^-3 |E|. In addition, our approach improves performance at an average rate of 1.8x for every doubling of threads.

cs.DC↗

arXiv2023

Enhancing Efficiency in Parallel Louvain Algorithm for Community Detection

Community detection is a key aspect of network analysis, as it allows for the identification of groups and patterns within a network. With the ever-increasing size of networks, it is crucial to have fast algorithms to analyze them efficiently. It is a modularity-based greedy algorithm that divides a network into disconnected communities better over several iterations. Even in big, dense networks, it is renowned for establishing high-quality communities. However it can be at least a factor of ten slower than community discovery techniques that rely on label-propagation, which are generally extremely fast but obtain communities of lower quality. The researchers have suggested a number of methods for parallelizing and improving the Louvain algorithm. To decide which strategy is generally the best fit and which parameter values produce the highest performance without compromising community quality, it is critical to assess the performance and accuracy of these existing approaches. As we implement the single-threaded and multi-threaded versions of the static Louvain algorithm in this report, we carefully examine the method's specifics, make the required tweaks and optimizations, and determine the right parameter values. The tolerance between each pass can be changed to adjust the method's performance. With an initial tolerance of 0.01 and a tolerance decline factor of 10, an asynchronous version of the algorithm produced the best results. Generally speaking, according to our findings, the approach is not well suited for shared-memory parallelism; however, one potential workaround is to break the graph into manageable chunks that can be independently executed and then merged back together.

cs.SI↗

arXiv2023

Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection

Community detection is an essential task in network analysis as it helps identify groups and patterns within a network. High-speed community detection algorithms are necessary to analyze large-scale networks in a reasonable amount of time. Researchers have made significant contributions in the development of high-speed community detection algorithms, particularly in the area of label-propagation based disjoint community detection. These algorithms have been proven to be highly effective in analyzing large-scale networks in a reasonable amount of time. However, it is important to evaluate the performance and accuracy of these existing methods to determine which algorithm is best suited for a particular type of network and specific research problem. In this report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based static community discovery techniques. We pay close attention to each algorithm's minute details as we implement both its single-threaded and multi-threaded OpenMP-based variants, making any necessary adjustments or optimizations and obtaining the right parameter values. The RAK algorithm is found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with 12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA algorithm works well with a single label for road networks and max labels of 4-16 for other classes of graphs. The SLPA algorithm performs well with increasing memory size, but overall doesn't offer a favourable return on investment. The RAK algorithm is recommended for label-propagation based disjoint community detection.

cs.SI↗

arXiv2021

Effect of stepwise adjustment of Damping factor upon PageRank

The effect of adjusting damping factor α, from a small initial value α0 to the final desired αf value, upon then iterations needed for PageRank computation is observed. Adjustment of the damping factor is done in one or more steps. Results show no improvement in performance over a fixed damping factor based PageRank.

cs.DM↗

arXiv2021

Adjusting PageRank parameters and Comparing results

The effect of adjusting damping factor α and tolerance τ on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L{\infty} norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor α linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance τ exponentially decreases the iterations needed almost exponentially. On average, PageRank with L{\infty} norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance τ values, convergence can occur in a single iteration. On the contrary, below certain tolerance τ values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit without convergence. The six mean ratios for relative performance comparison are based on arithmetic, geometric, and harmonic mean, as well as the order of ratio calculation. Among them GM-RATIO, geometric mean followed by ratio calculation, is found to be most stable, followed by AM-RATIO.

cs.DM↗