Abstract
In this paper, we are interested in how to explore and utilize the relationship between network communities and semantic topics in order to find the strong explanatory communities robustly. First, the relationship between communities and topics displays different situations. For example, from the viewpoint of semantic mapping, their relationship can be one-to-one, one-to-many or many-to-one. But from the standpoint of underlying community structures, the relationship can be consistent, partially consistent or completely inconsistent. Second, it will be helpful to not only find communities more precise but also reveal the communities’ semantics that shows the relationship between communities and topics. To better describe this relationship, we introduce the transition probability which is an important concept in Markov chain into a well-designed nonnegative matrix factorization framework. This new transition probability matrix with a suitable prior which plays the role of depicting the relationship between communities and topics can perform well in this task. To illustrate the effectiveness of the proposed new approach, we conduct some experiments on both synthetic and real networks. The results show that our new method is superior to baselines in accuracy. We finally conduct a case study analysis to validate the new method’s strong interpretability to detected communities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)
Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821–7826 (2002)
Jia, S., Gao, L., Gao, Y., et al.: Defining and identifying cograph communities in complex networks. New J. Phys. 17(1), 013044 (2015)
Yang, L., Cao, X., He, D., et al.: Modularity based community detection with deep learning. In: International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, pp. 2252–2258 (2016)
Fanuel, M., Alaiz, C.M., Suykens, J.A., et al.: Magnetic eigenmaps for community detection in directed networks. Phys. Rev. E 95(2), 022302 (2017)
Hao, F., Min, G., Pei, Z., et al.: K-clique community detection in social networks based on formal concept analysis. IEEE Syst. J. 11(1), 250–259 (2017)
Whang, J.J., Gleich, D.F., Dhillon, I.S., et al.: Overlapping community detection using neighborhood-inflated seed expansion. IEEE Trans. Knowl. Data Eng. 28(5), 1272–1284 (2016)
Jin, D., Wang, H., Dang, J., et al.: Detect overlapping communities via modeling and ranking node popularities. In: 30th AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 172–178 (2016)
Van Laarhoven, T., Marchiori, E.: Local network community detection with continuous optimization of conductance and weighted kernel k-means. J. Mach. Learn. Res. 17(147), 1–28 (2016)
Jin, D., Wang, X., He, R., et al.: Robust detection of link communities in large social networks by exploiting link semantics. In: 32th AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA (2018)
He, D., Feng, Z., Jin, D., et al.: Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In: 31th AAAI Conference on Artificial Intelligence, San Francisco, California, USA (2017)
Akbari, M., Chua, T.S.: Leveraging behavioral factorization and prior knowledge for community discovery and profiling. In: Web Search and Data Mining (WSDM), UK, pp. 71–79 (2017)
Cai, H., Zheng, V.W., Zhu, F., et al.: From community detection to community profiling. Proc. VLDB Endow. 10(7), 817–828 (2017)
Wang, X., Jin, D., Cao, X., et al.: Semantic community identification in large attribute networks. In: 30th AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 265–271 (2016)
Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5(6), 927–935 (1992)
Hunter, D.R., Lange, K.A.: A tutorial on mm algorithms. Am. Stat. 58(1), 30–37 (2004)
Sen, P., Namata, G., Bilgic, M., et al.: Collective classification in network data. AI Mag. 29(3), 93–106 (2008)
Leskovec, J.: Stanford Network Analysis Project (2016). http://snap.stanford.edu
Liu, H., Wu, Z., Li, X., et al.: Constrained nonnegative matrix factorization for image representation. IEEE Trans. Softw. Eng. 34(7), 1299–1311 (2012)
Yang, J., Mcauley, J., Leskovec, J., et al.: Community detection in networks with node attributes. In: the IEEE International Conference on Data Mining series (ICDM), Dallas, Texas, USA, pp. 1151–1156 (2013)
Karrer, B., Newman, M.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Web Search and Data Mining (WSDM), Rome, Italy, pp. 587–596 (2013)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Yang, J., Mcauley, J., Leskovec, J., et al.: Community detection in networks with node attributes. In: the IEEE International Conference on Data Mining series (ICDM), Dallas, Texas, USA, pp. 1151–1156 (2013)
Pool, S., Bonchi, F., Van Leeuwen, M., et al.: Description-driven community detection. ACM Trans. Intell. Syst. Technol. 5(2), 1–28 (2014)
Yang, T., Jin, R., Chi, Y., et al.: Combining link and content for community detection: a discriminative approach. In: 13th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Paris, France, pp. 927–936 (2009)
Balasubramanyan, R., Cohen, W.W.: Block-LDA: jointly modeling entity-annotated text and entity-entity links. In: SIAM International Conference on Data Mining (SDM), Mesa, Arizona, USA, pp. 450–461 (2011)
Kido, G.S., Igawa, R.A., Barbon Jr, S.: Topic modeling based on louvain method in online social networks. In: Proceedings of XII Brazilian Symposium on Information Systems, Florianópolis, SC, pp. 353–360 (2016)
Acknowledgment
This work was supported by the National Key R&D Program of China (2017YFC0820106), the Natural Science Foundation of China (61502334, 61772361, 61673293) and the Elite Scholar Program of Tianjin University (2017XRG-0016).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Jin, D., Liu, Z., He, D., Gabrys, B., Musial, K. (2018). Robust Detection of Communities with Multi-semantics in Large Attributed Networks. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-99365-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99364-5
Online ISBN: 978-3-319-99365-2
eBook Packages: Computer ScienceComputer Science (R0)