現今社群網路蓬勃發展，對於網路中社群偵測的相關研究日益受到研究者的關注，而過去許多相關研究僅利用拓樸網路中的架構來進行社群偵測，在本論文中，我們提出更改邊的權重之演算法，將社群網路中用戶的特徵以及拓樸網路中的架構，同時列入分群的考量之中，並且利用機器學習中的特徵選擇方法有效避免維數災難以及過濾網路中的噪音，接著選擇較有影響力的特徵進行更改邊的權重運算，利用此兩步驟的預先處理能有效提升標準化互信息的值，讓分群結果與真實分群結果更為相近。

Community detection is a long standing problem in data mining and machine learning. In recent years, lots of researchers focused on community detection in social networks. But most of community detection approaches are only based on topological structure of community. In this paper, we proposed an extension algorithm to modify the weight of each edge in the network which let the community detection problem of attributed networks consider not only topological structure but also attributes that describe the properties of each vertex. Moreover, we used feature selection in machine learning to avoid the curse of dimensionality and reduce the noises (e.g. unimportant features) in the network. After that, we got the importance of each feature and modified the edges weight for Top - N features. We experimentally evaluated our approach on a real-world social network. The obtained results showed that our two-step preprocessing method can easily improve Normalized Mutual Information (NMI) value and make the partition results be more similar to the ground truth.

摘要
目錄
1. Introduction --------------------1
2. Related Works -------------------4
3. Fundamental Concepts ------------7
4. Design and Implementation ------19
5. Performance Evaluation ---------26
6. Conclusion ---------------------37

M. Newman, Networks: an introduction. Oxford university press, 2010.

A. Lancichinetti and S. Fortunato, “Community detection algorithms: A comparative analysis,”Physical Review E, Vol. 80, No. 5, p. 056117, 2009.

Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M. “Extending the definition of modularity to directed graphs with overlapping communities,”Journal of Statistical Mechanics: Theory and Experiment, p. 03024, 2009.

Zarei, M., Izadi, D., Samani, K. A. "Detecting overlapping community structure of networks based on vertex–vertex correlations,”Journal of Statistical Mechanics: Theory and Experiment, p. 11013, 2009.

Rosvall, Martin, and Carl T. Bergstrom, "Maps of random walks on complex networks reveal community structure,”Proceedings of the National Academy of Sciences, Vol. 105 No. 4, pp.1118-1123, 2008.

Brandes, Ulrik, et al. , “On modularity clustering,”IEEE Transactions on Knowledge and Data Engineering, Vol. 20, No. 2, pp. 172-188, 2008.

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre, “Fast unfolding of communities in large networks,”Journal of Statistical Mechanics: Theory and Experiment, p. 10008, pp. 1-12 , 2008.

Palla, G. and Derényi, I. and Farkas, I. and Vicsek, T. “Uncovering the overlapping community structure of complex networks in nature and society,”Nature, Vol. 435, pp. 814-818, 2005.

J. Xie, B. K. Szymanski and X. Liu, “SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-Listener Interaction Dynamic Process,”IEEE ICDM Workshop on DMCCI, pp. 344-349, 2011.

A Lancichinetti, F Radicchi, JJ Ramasco, S Fortunato, “Finding statistically significant communities in networks,”PloS one, Vol. 6, No. 4, p. e18961, 2011.

Bellman, R. and Bellman, R.E., Adaptive Control Processes: A Guided Tour, Princeton University Press, 1961

J. Yang, J. McAuley, and J. Leskovec,“Community detection in networks with node attributes,”IEEE International Conference on Data Mining (ICDM), pp. 1151–1156, 2013


J. Chen, O. R. Za¨ıane, and R. Goebel, “Local community identification in social networks,”ASONAM, pp. 237–242, 2009.

M. Chen, K. Kuzmin, B. K. Szymanski, “Community detection via Maximization of Modularity and Its Variants,”IEEE Trans. Computation Social Systems, Vol. 1, No. 1, pp. 46-65, 2014.

R. Cazabet; F. Amblard; C. Hanachi; , “Detection of Overlapping Communities in Dynamical Social Networks,”Social Computing (SocialCom), pp.309-314, 2010.

J. Yang, J. McAuley, and J. Leskovec, “Community detection in networks with node attributes,”Data mining (ICDM), 2013 ieee 13th international conference on. IEEE, pp. 1151–1156, 2013.

Y. Asim, A. Majeed, B. Raza, R. Ghazal, W. Naeem, and A. K. Malik,“Community Detection in Networks using Node Attributes and Modularity,”International Journal of Advanced Computer Science and Applications(IJACSA), Vol. 8, No. 1, 2017

K. Steinhaeuser, and N. V. Chawla, “Community Detection in a Large Real-World Social Network,”International Conference on Social Computing, pp. 168-175, 2008.

T. A. Dang, E. Viennet,“Community Detection based on Structural and Attribute Similarities,”International Conference on Digital Society (ICDS), pp. 7–14, 2012

H. Elhadi, G. Agam,“Structure and Attributes Community Detection:
Comparative Analysis of Composite, Ensemble and Selection Methods,”Proceedings of the 7th Workshop on Social Network Mining
and Analysis (SNAKDD ’13). ACM, pp.1-10, 2013

C. Bothorel, J. D. Cruz, M. Magnani, and B. Micenkova, “Clustering attributed graphs: models, measures and methods,”Network Science 3(3), pp. 408–444, 2015

M. E. J. Newman, “Coauthorship networks and patterns of scientific collaboration,”PNAS, pp. 5200-5205, 2004.


F. Pedregosa et al.“Scikit-learn: Machine Learning in Python,”Journal of Machine Learning Research, pp. 2825-2830, 2011.

L. Buitinck et al.“API design for machine learning software: experiences from the scikit-learn project,”arXiv preprint arXiv:1309.0238, 2013

Machine Learning: Python. (2015, December 21). [Online]. Available: https://machine-learning-python.kspax.io/.

Deng, K.,“OMEGA: On-line Memory-Based General Purpose System Classifier,”
Doctor, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
(1998)

L. Breiman,“Bagging predictors. Machine Learning,”pp. 123–140, 1996.

Balazs Holczer, Random Forest Classifier – Machine Learning (2018, February 23). [Online]. Available: http://www.globalsoftwaresupport.com/random-forest-classifier-bagging-machine-learning/.

V. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre,“Fast unfolding of communities in large networks,”Journal of Statistical Mechanics: Theory and Experiment, p. P10008, 2008.

Ferrara, E., De Meo, P., Fiumara, G., Provetti, A.“The role of strong and weak ties in facebook: a community structure perspective,” Computational Approaches to Social Modeling (ChASM), 2012

P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti. “Generalized louvain method for community detection in large networks,”Proc. 11th International Conference On Intelligent Systems Design And Applications, pp. 88–93. IEEE, 2011.

Jierui Xie, Boleslaw K. Szymanski and Xiaoming Liu, “SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process,”IEEE ICDM workshop on DMCCI, 2011.

Jierui Xie and Boleslaw K. Szymanski, “Towards Linear Time Overlapping Community Detection in Social Networks,”16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2012.

Jierui Xie, Stephen Kelley and Boleslaw K. Szymanski, “Overlapping Community Detection in Networks: the State of the Art and Comparative Study,”ACM Computing Surveys 45(12), 2013.

U. N. Raghavan, R. Albert, and S. Kumara, “Near linear time algorithm to detect community structures in large-scale networks,” Phys. Rev. E, Vol. 76, p. 036106, 2007.

M. E. J. Newman, M. Girvan,“Finding and evaluating community structure in networks,”Phys. Rev. E, 69:026113, 2004

M. E. J. Newman,“Modularity and community structure in networks,”
Proc. Natl. Acad. Sci. USA in press, 2006.

Lancichinetti A, Fortunato S and Kertész J,“Detecting the overlapping and hierarchical community structure in complex networks,”New J. Phys, 11 033015, 2009

B. Martinez-Seis, X. Li, “Ranking features in Facebook to detect overlapping communities,”2016 IEEE 13th International Conference on Networking Sensing and Control (ICNSC), pp. 1-6, 2016.

S. Harenberg, G. Bello, L. Gjeltema, S. Ranshous, J. Harlalka, R. Seay, K. Padmanabhan, and N. Samatova,“Community detection in large-scale networks: a survey and empirical evaluation,”Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 6, no. 6, pp. 426-439, 2014