SchneiderFFeldmannAKrishnamurthyB, et al. Understanding online social network usage from a network perspective. In: The ninth ACM SIGCOMM conference on internet measurement conference (IMC '09), 2009, pp. 35–48.
2.
FortunatoS.Community detection in graphs. Phys Rep2010;
486: 75–174.
3.
HawkinsDM.Identification of outliers.
London:
Chapman and Hall, 1980.
4.
Qi GJ, Aggarwal CC and Huang T. Community detection with edge content in social media networks. In: IEEE 28th International Conference on Data Engineering, 1 April 2012, pp. 534--545.
5.
Adnan M, Alhajj R and Rokne J. Identifying social communities by frequent pattern mining. In: IEEE 13th International Conference Information Visualisation, 15 July 2009, pp. 413--418. IEEE.
6.
Kanawati R. Licod: Leaders identification for community detection in complex networks. In: IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, 9 October 2011, pp. 577--582. IEEE.
7.
Goyal A, Bonchi F, Lakshmanan LV. Discovering leaders from community actions. In: 17th ACM conference on Information and knowledge management, 26 October 2008, pp. 499--508. ACM.
8.
Khorasgani RR, Chen J, Zaïane OR. Top leaders community detection approach in information networks. In: 4th Workshop on Social Network Mining and Analysis, 25 July 2010, p. 228.
9.
ZhugeH.Communities and emerging semantics in semantic link network: discovery and learning. IEEE Trans Knowl Data Eng2009;
21: 785–799.
10.
LuDLiQLiaoSS.A graph-based action network framework to identify prestigious members through member’s prestige evolution. Decis Support Syst2012;
53: 44–54.
11.
HofmanJMWigginsCH.Bayesian approach to network modularity. Phys Revi Lett2008;
100: 258701.
12.
ClausetAMooreCNewmanME.Hierarchical structure and the prediction of missing links in networks. Nature2008;
453: 98–101.
13.
Keith H, Tina E-R, Spiros P, et al. HCDF: A hybrid community discovery framework. In: The proceedings of the 10th SIAM international conference on data mining, SDM 2010, pp.754-765.
14.
Berlingerio M, Bonchi F, Bringmann B, et al. Mining Graph Evolution Rules. In: Machine Learning and Knowledge Discovery in Databases. ECML PKDD (eds. Buntine W, Grobelnik M, Mladenić D, Shawe-Taylor J), Springer, Berlin, Heidelberg, 7--11 September 2009, pp 115--130.
15.
KuramochiMKarypisG.Finding frequent patterns in a large sparse graph. Data Min Knowl Discov2005;
11: 243–271.
16.
NijssenSKokJN. A quickstart in frequent structure mining can make a difference. In: The proceedings of the tenth ACMSIGKDD international conference on knowledge discovery and data mining, 2004.
17.
Yan X and Han J. gspan: Graph-based substructure pattern mining. In: IEEE International Conference on Data Mining, 9 December 2002, pp. 721--724. IEEE.
18.
ShenHChengXCaiK, et al.
Detect overlapping and hierarchical community structure in networks. Phys A Stat Mech Appl2009;
388: 1706–1712.
SaitoKYamadaTKazamaK. Extracting communities from complex networks by the k-dense method. IEICE Trans Fundam Electron Commun Comput Sci 2008; 91(11): 3304--3311.
BorgattiSPEverettMG.Graph colorings and power in experimental exchange networks. Soc Networks1992;
14: 287–308.
23.
EverettMGBorgattiSP.Exact colorations of graphs and digraphs. Soc Networks1996;
18:319–331.
24.
ClaffertyEM.Facilitating social networking within the student experience. Int J Elec Eng Educ2011;
48: 245–251.
25.
Palla G, Derényi I, Farkas I and Vicsek T. Uncovering the overlapping community structure of complex networks in nature and society. Nature 2005; 435 (7043): 814--818.
26.
BronCKerboschJ.Finding all cliques of an undirected graph. Commun ACM1973;
16: 575–577.
27.
Troussas C, Virvou M, Caro J, et al. Mining relationships among user clusters in Facebook for language learning. In: International Conference on Computer, Information and Telecommunication Systems (CITS), 7 May 2013, pp. 1--5. IEEE.
28.
Zhou D, Manavoglu E, Li J, et al. Probabilistic models for discovering e-communities. In: The proceedings of the 15th international conference on World Wide Web, 23 May 2006, pp. 173--182. ACM.
29.
De MeoPNoceraATerracinaG, et al.
Recommendation of similar users, resources and social networks in a social internetworking scenario. Inf Sci2011;
181: 1285–1305.
30.
ChandolaVBanerjeeAKumarV.Anomaly detection: a survey. ACM Comput Surv2009;
41: 1–58.
31.
ViswanathB, et al. Towards detecting anomalous user behavior in online social networks. In: The twenty third USENIX Security Symposium (SEC'14), 2014, pp. 223–238.
32.
Bogdanov P, Busch M, Moehlis J, et al. The social media genome: Modeling individual topic-specific behavior in social media. In: IEEE international conference on advances in social networks analysis and mining, 25 August 2013, pp. 236--242. ACM.
33.
Ahmad MA, Keegan B, Roy A, Williams D, Srivastava J and Contractor N. Guilt by association?: network based propagation approaches for gold farmer detection. In: IEEE/ACM international conference on advances in social networks analysis and mining, 25 August 2013, pp. 121--126. ACM.
34.
O'Banion S and Birnbaum L. Using explicit linguistic expressions of preference in social media to predict voting behavior. In: IEEE/ACM international conference on advances in social networks analysis and mining, 25 August 2013, pp. 207--214. ACM.
35.
PanBZhangLSmithK.A mixed-method study of user behavior and usability on an online travel agency. Inf Technol Tour2012;
13: 353–364.
36.
Noble CC and Cook DJ. Graph-based anomaly detection. In: The proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 24 August 2003, pp. 631--636. ACM.
37.
GaoJDuNFanW, et al. A multi-graph spectral framework for mining multi-source anomalies. In: Graph embedding for pattern analysis. New York: Springer, 2013.
38.
SavageDZhangXYuX, et al.
Anomaly detection in online social networks. Soc Networks2014;
39: 62–70.
Das K and Schneider J. Detecting anomalous records in categorical datasets. In: The proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 12 August 2007, pp. 220--229. ACM.
45.
KaurRSinghS.A survey of data mining and social network analysis based anomaly detection techniques. Egypt Informatics J2016;
17: 199–216.
46.
GetoorLDiehlCP.Link mining: a survey. ACM SIGKDD Explor Newslett2005;
7: 3–12.
47.
VigliottiMGHankinC.Discovery of anomalous behavior in temporal networks. Soc Networks2015;
41: 18–25.
48.
Lee H-Y, Kim D-H and Park K-R. Pest diagnosis system based on deep learning using collective intelligence. International Journal of Electrical Engineering & Education. DOI: 10.1177/0020720919833052.
49.
GaoJLiangFFanW, et al. On community outliers and their efficient detection in information networks. In: The sixteenth ACM SIGKDD international conference on Knowledge discovery and data mining – KDD (KDD '10), 2010, pp. 813–822.
50.
Ahmad MoosaviSJalaliMMisaghianN, et al.
Community detection in social networks using user frequent pattern mining. Knowl Inf Syst2017;
51: 159–186.
51.
MisloveAMarconMGummadiK, et al. Measurement and analysis of online social networks. In: Proceedings of the seventh ACM SIGCOMM conference on internet measurement, San Diego, CA, USA, 2007.
52.
WittenIHFrankE.Data mining: practical machine learning tools and techniques.
San Francisco:
Morgan Kauf-mann, 2005.