Abstract
Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods.
Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log4 n) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.
- V. Agarwal, F. Petrini, D. Pasetto, and D. A. Bader. 2010. Scalable graph exploration on multicore processors. In SC. IEEE, 1--11. Google Scholar
Digital Library
- D. A. Bader and K. Madduri. 2006. Gtgraph: A synthetic graph generator suite. Atlanta, GA, February (2006).Google Scholar
- C. Borgs, M. Brautbar, J. Chayes, and B. Lucier. 2014. Maximizing Social Influence in Nearly Optimal Time. In SODA. SIAM, 946--957. Google Scholar
Digital Library
- A. Campan. 2008. A clustering approach for data and structural anonymity in social networks. PinKDD (2008), 54.Google Scholar
- M. Cha, A. Mislove, and K. P. Gummadi. 2009. A measurement-driven analysis of information propagation in the flickr social network. In WWW. ACM, 721--730. Google Scholar
Digital Library
- W. Chen, C. Wang, and Y. Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD. ACM, 1029--1038. Google Scholar
Digital Library
- F. Chung and L. Lu. 2006. Concentration inequalities and martingale inequalities: a survey. Internet Mathematics (2006), 79--127.Google Scholar
- E. Cohen, D. Delling, T. Pajor, and R. F. Werneck. 2014. Sketch-based influence maximization and computation: Scaling up with guarantees. In CIKM. ACM, 629--638. Google Scholar
Digital Library
- P. Dagum, R. Karp, M. Luby, and S. Ross. 2000. An Optimal Algorithm for Monte Carlo Estimation. SICOMP (2000), 1484--1496. Google Scholar
Digital Library
- D. J. Daley, J. Gani, and J. M. Gani. 2001. Epidemic modelling: an introduction. Vol. 15. Cambridge University Press.Google Scholar
- T. N. Dinh and M. T. Thai. 2015. Assessing attack vulnerability in networks with uncertainty. In INFOCOM. IEEE, 2380--2388.Google Scholar
- N. Du, L. Song, M. Gomez-Rodriguez, and H. Zha. 2013. Scalable influence estimation in continuous-time diffusion networks. In NIPS. 3147--3155. Google Scholar
Digital Library
- A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. 2010. Learning Influence Probabilities in Social Networks. In WSDM. ACM, 241--250. Google Scholar
Digital Library
- P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R. Zadeh. 2013. Wtf: The who to follow service at twitter. In WWW. ACM, 505--514. Google Scholar
Digital Library
- S. Hong, Sang K. Kim, T. Oguntebi, and K. Olukotun. 2011. Accelerating CUDA graph algorithms at maximum warp. In SIGPLAN Notices. ACM, 267--276. Google Scholar
Digital Library
- Y. M. Ioannides and L. L. Datcher. 2004. Job information networks, neighborhood effects, and inequality. Journal of economic literature (2004), 1056--1093.Google Scholar
- D. Kempe, J. Kleinberg, and É. Tardos. 2003. Maximizing the spread of influence through a social network. In KDD. 137--146. Google Scholar
Digital Library
- D. Kempe, J. Kleinberg, and E. Tardos. 2005. Influential nodes in a diffusion model for social networks. In ICALP. 1127--1138. Google Scholar
Digital Library
- A. Krause, A. Singh, and C. Guestrin. 2008. Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. JMLR (2008), 235--284. Google Scholar
Digital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. 2010. What is Twitter, a social network or a news media?. In WWW. ACM, 591--600. Google Scholar
Digital Library
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. 2007. Cost-effective outbreak detection in networks. In KDD. ACM, 420--429. Google Scholar
Digital Library
- J. Lin and M. Schatz. 2010. Design patterns for efficient graph algorithms in MapReduce. In MLG. ACM, 78--85. Google Scholar
Digital Library
- B. Lucier, J. Oren, and Y. Singer. 2015. Influence at scale: Distributed computation of complex contagion in networks. In KDD. ACM, 735--744. Google Scholar
Digital Library
- M. Mitzenmacher and E. Upfal. 2005. Probability and computing: Randomized algorithms and probabilistic analysis. Cambridge University Press. Google Scholar
Digital Library
- D. T. Nguyen, Z. Huiyuan, S. Das, M. T. Thai, and T. N. Dinh. 2013. Least Cost Influence in Multiplex Social Networks: Model Representation and Analysis. In ICDM. 567--576.Google Scholar
- H. T. Nguyen, P. Ghosh, M. L. Mayo, and T. N. Dinh. 2016. Multiple Infection Sources Identification with Provable Guarantees. In CIKM. ACM, 1663--1672. Google Scholar
Digital Library
- H. T. Nguyen, M. T. Thai, and T. N. Dinh. 2016. Cost-aware targeted viral marketing in billion-scale networks. In INFOCOM. IEEE, 1--9.Google Scholar
- H. T. Nguyen, M. T. Thai, and T. N. Dinh. 2016. Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks. In SIGMOD. ACM, 695--710. Google Scholar
Digital Library
- N. Ohsaka, T. Akiba, Y. Yoshida, and K. Kawarabayashi. 2016. Dynamic influence analysis in evolving networks. VLDB (2016), 1077--1088. Google Scholar
Digital Library
- V. M. Preciado, M. Zargham, C. Enyioha, A. Jadbabaie, and G. Pappas. 2013. Optimal vaccine allocation to control epidemic outbreaks in arbitrary networks. In CDC. IEEE, 7486--7491.Google Scholar
- Y. Tang, Y. Shi, and X. Xiao. 2015. Influence Maximization in Near-Linear Time: A Martingale Approach. In SIGMOD. ACM, 1539--1554. Google Scholar
Digital Library
- Y. Tang, X. Xiao, and Y. Shi. 2014. Influence maximization: Near-optimal time complexity meets practical efficiency. In SIGMOD. ACM, 75--86 Google Scholar
Digital Library
Index Terms
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Recommendations
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Performance evaluation reviewEstimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new ...
Outward Influence and Cascade Size Estimation in Billion-scale Networks
SIGMETRICS '17 Abstracts: Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer SystemsEstimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new ...
Prediction of retweet cascade size over time
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementRetweet cascades play an essential role in information diffusion in Twitter. Popular tweets reflect the current trends in Twitter, while Twitter itself is one of the most important online media. Thus, understanding the reasons why a tweet becomes ...






Comments