ABSTRACT
Computing the set of influential nodes of a given size, which when activated will ensure maximal spread of influence on a complex network, is a challenging problem impacting multiple applications. A rigorous approach to influence maximization involves utilization of optimization routines that come with a high computational cost. In this work, we propose to exploit the existence of communities in complex networks to accelerate the mining of influential seeds. We provide intuitive reasoning to explain why our approach should be able to provide speedups without significantly degrading the extent of the spread of influence when compared to the case of influence maximization without using the community information. Additionally, we have parallelized the complete workflow by leveraging an existing parallel implementation of the Louvain community detection algorithm. We then conduct a series of experiments on a dataset with three representative graphs to first verify our implementation and then demonstrate the speedups. Our method achieves speedups ranging from 3x to 28x for graphs with small number of communities while nearly matching or even exceeding the activation performance on the entire graph. Complexity analysis reveals that dramatic speedups are possible for larger graphs that contain a correspondingly larger number of communities. In addition to the speedups obtained from the utilization of the community structure, scalability results show up to 6.3x speedup on 20 cores relative to the baseline run on 2 cores. Finally, current limitations of the approach are outlined along with the planned next steps.
- Michelle Girvan and Mark EJ Newman. Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12):7821--7826, 2002.Google ScholarCross Ref
- David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a social network. In ACM SIGKDD, pages 137--146, New York, NY, USA, 2003. ACM. Google ScholarDigital Library
- Edith Cohen, Daniel Delling, Thomas Pajor, and Renato F. Werneck. Sketch-based influence maximization and computation: Scaling up with guarantees. In ACM SIGKDD, CIKM '14, pages 629--638, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- Christian Borgs, Michael Brautbar, Jennifer Chayes, and Brendan Lucier. Maximizing social influence in nearly optimal time. In ACM-SIAM Symposium on Discrete Algorithms, SODA '14, pages 946--957. SIAM, 2014. Google ScholarDigital Library
- R. Lambiotte, J.-C. Delvenne, and M. Barahona. Random walks, markov processes and the multiscale modular organization of complex networks. Network Science and Engineering, IEEE Transactions on, 1(2):76--90, 2014.Google Scholar
- Martin Rosvall and Carl T Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118--1123, 2008.Google ScholarCross Ref
- James P Gleeson. Cascades on correlated and modular random networks. Physical Review E, 77(4):046117, 2008.Google ScholarCross Ref
- Aram Galstyan and Paul Cohen. Cascading dynamics in modular networks. Physical Review E, 75(3):036109, 2007.Google ScholarCross Ref
- Marcel Salathé and James H Jones. Dynamics and control of diseases in networks with community structure. PLoS Comput Biol, 6(4):e1000736, 2010.Google ScholarCross Ref
- Pedro Domingos and Matt Richardson. Mining the network value of customers. In ACM SIGKDD, pages 57--66. ACM, 2001. Google ScholarDigital Library
- Pierre L'Ecuyer. Efficient and portable combined random number generators. Communications of the ACM, 31(6):742--751, 1988. Google ScholarDigital Library
- Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.Google ScholarCross Ref
- Hao Lu, Mahantesh Halappanavar, and Ananth Kalyanaraman. Parallel heuristics for scalable community detection. Parallel Computing, 47:19--37, 2015. Google ScholarDigital Library
- Andrea Lancichinetti and Santo Fortunato. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E, 80(1):016118, 2009.Google ScholarCross Ref
- Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi. Benchmark graphs for testing community detection algorithms. Physical review E, 78(4):046110, 2008.Google Scholar
- Lada A Adamic and Natalie Glance. The political blogosphere and the 2004 us election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery, pages 36--43. ACM, 2005. Google ScholarDigital Library
- Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014.Google Scholar
- William Webber, Alistair Moffat, and Justin Zobel. A similarity measure for indefinite rankings. ACM Transactions on Information Systems, 28(4):20, 2010. Google ScholarDigital Library
- Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance. Cost-effective outbreak detection in networks. In ACM SIGKDD, pages 420--429. ACM, 2007. Google ScholarDigital Library
- Wei Chen, Chi Wang, and Yajun Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In ACM SIGKDD, pages 1029--1038. ACM, 2010. Google ScholarDigital Library
- Masahiro Kimura and Kazumi Saito. Tractable models for information diffusion in social networks. In Knowledge Discovery in Databases: PKDD 2006, pages 259--271. Springer, 2006.Google Scholar
- Amit Goyal, Francesco Bonchi, and Laks VS Lakshmanan. A data-based approach to social influence maximization. VLDB Endowment, 5(1):73--84, 2011. Google ScholarDigital Library
- Fergal Reid and Neil Hurley. Diffusion in networks with overlapping community structure. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, pages 969--978. IEEE, 2011. Google ScholarDigital Library
- R. Lambiotte, J. C. Delvenne, and M. Barahona. Laplacian Dynamics and Multiscale Modular Structure in Networks, October 2009.Google Scholar
- Karine Nahon and Jeff Hemsley. Going viral. Polity, 2013. Google ScholarDigital Library
- Yu Wang, Gao Cong, Guojie Song, and Kunqing Xie. Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In ACM SIGKDD, pages 1039--1048. ACM, 2010. Google ScholarDigital Library
- Yi-Cheng Chen, Wen-Yuan Zhu, Wen-Chih Peng, Wang-Chien Lee, and Suh-Yin Lee. Cim: community-based influence maximization in social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 5(2):25, 2014. Google ScholarDigital Library
Index Terms
- Accelerating the mining of influential nodes in complex networks through community detection
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