Detecting multiple stochastic network motifs in network data

Knowledge and Information Systems

Volumn 42, Issue 1, 2015, Pages 49-74

  Liu, Kai ^a   Cheung, William K ^a   Liu, Jiming ^a  

^a HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Expectation maximization algorithms; Finite mixture models; Social networks; Stochastic network motifs

Indexed keywords

COMPLEX NETWORKS; COMPUTATIONAL EFFICIENCY; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; MIXTURES; PETROLEUM RESERVOIR EVALUATION; SIGNAL DETECTION; SOCIAL NETWORKING (ONLINE); STOCHASTIC SYSTEMS;

EFFICIENCY EVALUATION; EXPECTATION - MAXIMIZATIONS; EXPECTATION-MAXIMIZATION ALGORITHMS; FINITE MIXTURE MODELING; FINITE MIXTURE MODELS; ON-LINE SOCIAL NETWORKS; ROBUSTNESS AGAINST NOISE; STOCHASTIC NETWORKS;

STOCHASTIC MODELS;

EID: 84957964666     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-013-0680-4     Document Type: Article

References (50)

1. Newman M (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256
2. Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
3. Kleinberg J (1999) Hubs, authorities, and communities. ACM Comput Surv 31(4):5–7
4. Leskovec J, Lang K, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th international conference on World Wide Web, pp 631–640
5. Papadopoulos S, Kompatsiaris Y, Vakali A, Spyridonos P (2012) Community detection in social media. Data Min Knowl Discov 24(3):515–554
6. Comar P, Tan PN, Jain AK (2012) Simultaneous classification and community detection on heterogeneous network data. Data Min Knowl Discov 25(3):420–449
7. Shen-Orr S, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet 31(1):64–68
8. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827
9. Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Sheffer M, Alon U (2004) Superfamilies of evolved and designed networks. Science 303(5663):1538–1541
10. Kashtan N, Itzkovitz S, Milo R, Alon U (2004) Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11):1746–1758
11. Wernicke S (2006) Efficient detection of network motifs. IEEE/ACM Trans Comput Biol Bioinform 3(4):347–359
12. Berg J, Michael L (2004) Local graph alignment and motif search in biological networks. Proc Natl Acad Sci 101(41):14689–14694
13. Jiang R, Tu Z, Chen T, Sun F (2006) Network motif identification in stochastic networks. Proc Natl Acad Sci 103(25):9404–9409
14. Zhao Q, Tian Y, He Q, Oliver N, Jin R, Lee W (2010) Communication motifs: A tool to characterize social communications. In: Proccedings of the 19th ACM international conference on information and, knowledge management, pp 1645–1648
15. Kovanen L, Karsai M, Kaski K, Kertész J, Saramäki J (2011) Temporal motifs in time-dependent networks. J Stat Mech Theory Exp 11(11):5–23
16. Juszczyszyn K, Kazienko P, Musial K (2008) Local topology of social network based on motif analysis. In: Proceedings of knowledge-based intelligent information and engineering systems, pp 97–105
17. Musial K, Juszczyszyn K (2009) Motif-based analysis of social position influence on interconnection patterns in complex social network. In: Procceedings of the 1st Asian conference on intelligent information and database systems, pp 34–39
18. Braha D, Bar-Yam Y (2009) Time-dependent complex networks: dynamic centrality, dynamic motifs, and cycles of social interactions. Theory, models and applications, adaptive networks, pp 39–50
19. Leskovec J, Huttenlocher D, Kleinberg J (2010) Signed networks in social media. In: Proceedings of the 28th international conference on human factors in computing systems, pp 1361–1370
20. Clauset A (2005) Finding local community structure in networks. Phys Rev E 72(2):132–137
21. Faust K (2007) Very local structure in social networks. Sociol Method 37(1):209–256
22. Kossinets G, Kleinberg J, Watts D (2008) The structure of information pathways in a social communication network. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 435–443
23. Birmele E (2012) Detecting local network motifs. Electron J Stat 6(1):908–933
24. Gallos LK, Diego R, Fredrik L, Shlomo H, Hernán MA (2012) How people interact in evolving online affiliation networks. Phys Rev X 2(3):14–31
25. Allan E, Turkett W, Fulp E (2009) Using network motifs to identify application protocols. In: Global telecommunications conference, pp 1–7
26. Callaghan D, Harrigan M, Carthy J, Cunningham P (2012) Network analysis of recurring YouTube spam campaigns. In: Proceedings of the 6th international AAAI conference on weblogs and social media, pp 531–534
27. Liu K, Cheung WK, Liu J (2011) Stochastic network motif detection in social media. In: Proccedings of ICDM workshop on data mining in networks, pp 949–956
28. Liu K, Cheung W K, Liu J (2012) Detecting multiple stochastic network motifs in network data. In: Proceedings of the 16th Pacific–Asia conference on knowledge discovery and data mining, pp 205–217
29. Figueiredo M, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396
30. Bruno F, Palopoli L, Rombo S (2010) New trends in graph mining: structural and node-colored network motifs. Int J Knowl Discov Bioinform 1(1):81–99
31. Wong E, Baur B, Quader S, Huang C (2012) Biological network motif detection: principles and practice. Briefings Bioinform 12(2):202–215
32. Schreiber F, Schwöbbermeyer H (2005) Frequency concepts and pattern detection for the analysis of motifs in networks. Transactions on computational systems biology, pp 89–104
33. Schreiber F, Schwöbbermeyer H (2005) MAVisto: a tool for the exploration of network motifs. Bioinformatics 21(17):3572–3574
34. Chen J, Hsu W, Lee M, Ng S (2006) Nemofinder: dissecting genome-wide protein–protein interactions with meso-scale network motifs. In: Proccedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 106–115
35. Kashani Z, Ahrabian H, Elahi E et al (2009) Kavosh: a new algorithm for finding network motifs. BMC Bioinform 10(1):318–329
36. Grochow J, Kellis M (2007) Network motif discovery using subgraph enumeration and symmetry-breaking. Research in computational, molecular biology, pp 92–106
37. Omidi S, Schreiber F, Masoudi-Nejad A (2009) Moda: an efficient algorithm for network motif discovery in biological networks. Genes Genet Syst 84(5):385–395
38. Fortin S (1996) The graph isomorphism problem. Technical Report 96–20, University of Alberta, Edomonton, Alberta, Canada
39. McKay B (2007) Nauty user’s guide (version 2.4). Computer Science Dept, Australian National University
40. Vazquez A, Dobrin R, Sergi D, Eckmann J, Oltvai Z, Barabási A (2004) The topological relationship between the large-scale attributes and local interaction patterns of complex networks. Proc Natl Acad Sci 101(52):17940–17945
41. Tsourakakis CE (2011) Counting triangles in real-world networks using projections. Knowl Inf Syst 26(3):501–520
42. Kolountzakis M, Miller G, Peng R, Tsourakakis C (2012) Efficient triangle counting in large graphs via degree-based vertex partitioning. Internet Math 8(2):161–185
43. Seshadhri C, Pinar A, Kolda T (2013) Triadic measures on graphs: the power of wedge sampling. CoRR, abs/1202.5230
44. Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–38
45. Celeux G, Chrétien S, Forbes F, Mkhadri A (2001) A component-wise EM algorithm for mixtures. J Comput Graph Stat 10(4):697–712
46. Wallace C, Dowe D (1999) Minimum message length and kolmogorov complexity. Comput J 42(4):270–283
47. Cover T, Thomas J, Wiley J et al (1991) Elements of information theory. Wiley, New York
48. Leskovec J, Adamic L, Huberman B (2007) The dynamics of viral marketing. ACM Trans Web 1(1):5–44
49. Cartwright D, Harary F (1956) Structural balance: a generalization of Heider’s theory. Psychol Rev 63(5):277–293
50. Kumar N, Satoor S, Buck I (2009) Fast parallel expectation maximization for gaussian mixture models on GPUs using CUDA. In: Proccedings of the 11th IEEE international conference on high performance computing and communications, pp 103–109