GreediRIS: Scalable influence maximization using distributed streaming maximum cover

• Published in: Journal of parallel and distributed computing Vol. 198; p. 105037
• Main Authors: Barik, Reet, Cappa, Wade, Ferdous, S.M., Minutoli, Marco, Halappanavar, Mahantesh, Kalyanaraman, Ananth
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2025; Elsevier
• Subjects: Distributed influence maximization; Distributed submodular maximization; Parallel graph algorithms; Streaming maximum k-cover; Distributed submodular maximization; Streaming maximum k-cover; Distributed influence maximization; Parallel graph algorithms
• ISSN: 0743-7315
• DOI: 10.1016/j.jpdc.2025.105037

Influence maximization—the problem of identifying a subset of k influential seeds (vertices) in a network—is a classical problem in network science with numerous applications. The problem is NP-hard, but there exist efficient polynomial time approximations. However, scaling these algorithms still remain a daunting task due to the complexities associated with steps involving stochastic sampling and large-scale aggregations. In this paper, we present a new parallel distributed approximation algorithm for influence maximization with provable approximation guarantees. Our approach, which we call GreediRIS, leverages the RandGreedi framework—a state-of-the-art approach for distributed submodular optimization—for solving a step that computes a maximum k cover. GreediRIS combines distributed and streaming models of computations, along with pruning techniques, to effectively address the communication bottlenecks of the algorithm. Experimental results on up to 512 nodes (32K cores) of the NERSC Perlmutter supercomputer show that GreediRIS can achieve good strong scaling performance, preserve quality, and significantly outperform the other state-of-the-art distributed implementations. For instance, on 512 nodes, the most performant variant of GreediRIS achieves geometric mean speedups of 28.99× and 36.35× for two different diffusion models, over a state-of-the-art parallel implementation. We also present a communication-optimized version of GreediRIS that further improves the speedups by two orders of magnitude. •A new distributed streaming algorithm for RIS-based InfMax on distributed parallel platforms.•A truncation technique, to provide a knob to control communication overhead with a tradeoff in quality.•Presented algorithms have provable worst-case approximation guarantees.•Significant performance gains demonstrated on 512 compute nodes (32K cores) of a supercomputer.