Abstract
With the widespread of social platforms, event detection is becoming an important problem in social media. Yet, the large amount of content accumulated on social platforms brings great challenges. Moreover, the content usually is informal, lacks of semantics and rapidly spreads in dynamic networks, which makes the situation even worse. Existing approaches, including content-based detection and network structure-based detection, only use limited and single information of social platforms that limits the accuracy and integrity of event detection. In this paper, (1) we propose to model the entire social platform as a heterogeneous attribute graph (HAG), including types, entities, relations and their attributes; (2) we exploit non-parametric scan statistics to measure the statistical significance of subgraphs in HAG by considering historical information; (3) we transform the event detection in HAG into a densest subgraph discovery problem in statistical weighted network. Due to its NP-hardness, we propose an efficient approximate method to find the densest subgraphs based on (k, \(\varPsi \))-core, and simultaneously the statistical significance is guaranteed. In experiments, we conduct comprehensive empirical evaluations on Weibo data to demonstrate the effectiveness and efficiency of our proposed approaches.
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Berk, R.H., Jones, D.H.: Goodness-of-fit statistics that dominate the kolmogorov statistics. Probab. Theory Relat. Fields 47(1), 47–59 (1979). https://doi.org/10.1007/BF00533250
Chang, C., Zhang, Y., Szabo, C., Sheng, Q.Z.: Extreme user and political rumor detection on Twitter. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) ADMA 2016. LNCS (LNAI), vol. 10086, pp. 751–763. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49586-6_54
Chen, F., Neill, D.B.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In: SIGKDD, pp. 1166–1175. ACM (2014)
Fang, Y., Yu, K., Cheng, R., Lakshmanan, L.V., Lin, X.: Efficient algorithms for densest subgraph discovery. Proc. VLDB Endowment 12(11), 1719–1732 (2019)
Hamidian, S., Diab, M.T.: Rumor detection and classification for twitter data. CoRR abs/1912.08926 (2019)
Ji, F., Tay, W.P., Varshney, L.R.: An algorithmic framework for estimating rumor sources with different start times. IEEE Trans. Signal Process. 65(10), 2517–2530 (2017)
Jin, Z., Cao, J., Guo, H., Zhang, Y., Wang, Y., Luo, J.: Rumor detection on twitter pertaining to the 2016 U.S. presidential election. CoRR abs/1701.06250 (2017)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: DMCEEE, pp. 1103–1108 (2013)
Liang, G., He, W., Xu, C., Chen, L., Zeng, J.: Rumor identification in microblogging systems based on users’ behavior. TCSS 2(3), 99–108 (2015)
Neill, D.B., Lingwall, J.: A nonparametric scan statistic for multivariate disease surveillance. Adv. Dis. Surveill. 30(6), 106–110 (2007)
Pinto, P.C., Thiran, P., Verrerli, M.: Locating the source of diffusion in large-scale networks. CoRR abs/1208.2534 (2012)
Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)
Sun, S., Liu, H., He, J., Du, X.: Detecting event rumors on sina weibo automatically. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 120–131. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37401-2_14
Tam, N.T., Weidlich, M., Zheng, B., Yin, H., Hung, N.Q.V.: From anomaly detection to rumour detection using data streams of social platforms. VLDB 12(9), 1016–1029 (2019)
Wang, C.: Research on identifying information source in networks. Ph.D. thesis, University of Science and Technology of China, Hefei, China (2016)
Wu, K., Yang, S., Zhu, K.Q.: False rumors detection on sina weibo by propagation structures. In: ICDE, pp. 651–662 (2015)
Xu, W., Chen, H.: Scalable rumor source detection under independent cascade model in online social networks. In: MASN, pp. 236–242. IEEE Computer Society (2015)
Zhang, Y., Zhang, X., Zhang, B.: Observer deployment method for locating the information source in social network. J. Softw. 25, 2837–2851 (2014)
Zhang, Z., Xu, W., Wu, W., Du, D.-Z.: A novel approach for detecting multiple rumor sources in networks with partial observations. J. Comb. Optim. 33(1), 132–146 (2015). https://doi.org/10.1007/s10878-015-9939-x
Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: WWW, pp. 1395–1405. ACM (2015)
Zheng, L., Tan, C.W.: A probabilistic characterization of the rumor graph boundary in rumor source detection. In: ICDSP, pp. 765–769. IEEE (2015)
Zubiaga, A., Liakata, M., Procter, R.: Learning reporting dynamics during breaking news for rumour detection in social media. CoRR abs/1610.07363 (2016)
Acknowledgment
This research is partially supported by the National NSFC (61902004, 61672041, 61772124, 61732003, 61977001), National Key Research and Development Program of China (2018YFB1004402), Project of Beijing Municipal Education Commission (KM202010009009) and the Start-up Funds of North China University of Technology.
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Li, Y., Fan, X., Sun, J., Zhao, Y., Wang, G. (2020). Detecting Statistically Significant Events in Large Heterogeneous Attribute Graphs via Densest Subgraphs. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_10
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DOI: https://doi.org/10.1007/978-3-030-55130-8_10
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