Abstract
Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics. This problem has been proved to be NP-hard and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we encode the feature of each node by a vector and introduce a deep learning model, called deep-influence-evaluation-model (DIEM), to evaluate users’ influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.
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Albert, R., Barabási, A.: Statistical mechanics of complex networks. CoRR cond-mat/0106096 (2001)
Aslay, Ç., Barbieri, N., Bonchi, F., Baeza-Yates, R.A.: Online topic-aware influence maximization queries. In: Proceedings of the 17th International Conference on Extending Database Technology, EDBT 2014, Athens, Greece, 24–28 March 2014, pp. 295–306 (2014)
Bengio, Y., Courville, A.C., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held at Lake Tahoe, Nevada, United States, 5–8 December 2013, pp. 2787–2795 (2013)
Borgs, C., Brautbar, M., Chayes, J.T., Lucier, B.: Influence maximization in social networks: towards an optimal algorithmic solution. CoRR abs/1212.0884 (2012). http://arxiv.org/abs/1212.0884
Cai, H., Zheng, V.W., Chang, K.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Chen, W., Lin, T., Yang, C.: Real-time topic-aware influence maximization using preprocessing. In: Thai, M.T., Nguyen, N.P., Shen, H. (eds.) CSoNet 2015. LNCS, vol. 9197, pp. 1–13. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21786-4_1
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 28 June–1 July 2009, pp. 199–208 (2009)
Fan, J., et al.: OCTOPUS: an online topic-aware influence analysis system for social networks. In: 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, 16–19 April 2018, pp. 1569–1572 (2018)
Goyal, A., Lu, W., Lakshmanan, L.V.S.: CELF++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, 28 March–1 April 2011 (Companion Volume), pp. 47–48 (2011)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 855–864 (2016)
Guo, J., Zhang, P., Zhou, C., Cao, Y., Guo, L.: Personalized influence maximization on social networks. In: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, USA, 27 October–1 November 2013, pp. 199–208 (2013)
van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, 12–17 February 2016, pp. 2094–2100 (2016)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 24–27 August 2003, pp. 137–146 (2003)
Khalil, E.B., Dai, H., Zhang, Y., Dilkina, B., Song, L.: Learning combinatorial optimization algorithms over graphs. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017, pp. 6351–6361 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems. Proceedings of a Meeting Held at Lake Tahoe, Nevada, United States, 3–6 December 2012, pp. 1106–1114 (2012)
Kwak, H., Lee, C., Park, H., Moon, S.B.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26–30 April 2010, pp. 591–600 (2010)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J.M., Glance, N.S.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, 12–15 August 2007, pp. 420–429 (2007)
Li, G., Chen, S., Feng, J., Tan, K., Li, W.: Efficient location-aware influence maximization. In: International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, 22–27 June 2014, pp. 87–98 (2014)
Li, Y., Fan, J., Wang, Y., Tan, K.: Influence maximization on social graphs: a survey. IEEE Trans. Knowl. Data Eng. 30(10), 1852–1872 (2018)
Li, Y., Fan, J., Zhang, D., Tan, K.: Discovering your selling points: personalized social influential tags exploration. In: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017, Chicago, IL, USA, 14–19 May 2017, pp. 619–634 (2017)
Li, Y., Zhang, D., Tan, K.: Real-time targeted influence maximization for online advertisements. PVLDB 8(10), 1070–1081 (2015)
Mnih, V., et al.: Playing Atari with deep reinforcement learning. CoRR abs/1312.5602 (2013)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Nguyen, H.T., Dinh, T.N., Thai, M.T.: Cost-aware targeted viral marketing in billion-scale networks. In: 35th Annual IEEE International Conference on Computer Communications, INFOCOM 2016, San Francisco, CA, USA, 10–14 April 2016, pp. 1–9 (2016)
Schaul, T., Quan, J., Antonoglou, I., Silver, D.: Prioritized experience replay. CoRR abs/1511.05952 (2015)
Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction. Adaptive Computation and Machine Learning. MIT Press, Cambridge (1998)
Acknowledgements
This work is supported by the National Key Research and Development Program of China (Project Number: 2018YFB1003402), key projects of the national natural science foundation of China (Project Number: U1811263) and the Fundamental Research Funds for the Central Universities (Project Number: 2042017kf1017).
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Tian, S., Zhang, P., Mo, S., Wang, L., Peng, Z. (2019). A Learning Approach for Topic-Aware Influence Maximization. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_9
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