Advances in Networks
Volume 3, Issue 2, September 2015, Pages: 8-21
Received: Jul. 11, 2015;
Accepted: Jul. 23, 2015;
Published: Aug. 5, 2015
Views 3653 Downloads 147
Arnold Adimabua Ojugo, Dept. of Math/Computer, Federal University of Petroleum Resources Effurun, Delta State, Nigeria
Rume Elizabeth Yoro, Dept. of Computer Science, Delta State Polytechnic, Ogwashi-Uku, Delta State, Nigeria
Andrew Okonji Eboka, Dept. of Computer Sci. Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria
Mary Oluwatoyin Yerokun, Dept. of Computer Sci. Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria
Christiana Nneamaka Anujeonye, Dept. of Computer Sci. Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria
Fidelia Ngozi Efozia, Prototype Engineering Development Institute, Fed. Ministry of Science Technology, Osun State, Nigeria
Corruption is the bane of any economy. Its malady cuts across religious, socio-economic and political system of Nigeria. With a fast and contagious spread through the nation’s socio-economic and political strata, its adverse malignant effect is today, difficult to treat. This study models its contagion via an agent-based graph-diffusion model. Graphs are now quickly becoming the dominant life-form of most activities in a society, with human actors as nodes. Actors have ties that bind them to others via interaction as they form a social graph that analyzes the agent’s local feats via interaction to impact on the society as a global structure. Study explores the graph’s rich connective patterns and personal-networks as actors influence each other, so that graph’s behaviour evolves to orchestrate a relationship in probabilities of observed data and recognize patterns that aid decision making via its convergence to predict the expected number of final adopters as its optimal solution in a multi-peak function.
Arnold Adimabua Ojugo,
Rume Elizabeth Yoro,
Andrew Okonji Eboka,
Mary Oluwatoyin Yerokun,
Christiana Nneamaka Anujeonye,
Fidelia Ngozi Efozia,
Predicting Behavioural Evolution on a Graph-Based Model, Advances in Networks.
Vol. 3, No. 2,
2015, pp. 8-21.
Abraham, A., (2005). Handbook of Measuring System Design, John Wiley and Sons Ltd, ISBN: 0-470-02143-8, pp. 901 – 918.
Alpaydin, E., (2010). Introduction to Machine Learning, McGraw Hill publications, ISBN: 0070428077, NJ
Axelrod R., (1997). The Complexity of Cooperation. Princeton, NJ: Princeton Univ. Press
Beal, G.M and Bolen, J.M., (1955). How farm people accept new ideas, Ames, IA: Cooperative Extension Service Report 15.
Becker, M.H., (1970). Sociometric location and innovativeness: Reformulation and extension of the diffusion model, American Sociological Review, 35, p267-282.
Burt, R.S., (2004). Structural Holes and Good Ideas, American Journal of Sociology, 110(2), p349–399.
Clauset, A., Shalizi, C.R and Newman, M.E.J., (2009). Power-law distributions in empirical data, Siam Review, 51(4), p661–703.
Coleman J.S., (1990). Foundations of Social Theory. Cambridge, MA: Harvard Univ. Press.
David, P.C., (2007). Path dependence – a foundational concept for historical social sciences, Climetrica – Journal of Historical Economics and Econometric History, 1(2).
Davis, J.A., (1961). Locals and cosmopolitans in effects in American graduate schools, Int. Journal of Comparative Sociology, 2(2), p212-223.
Dozier, D.M., (1977). Communication Networks and threshold role in the adoption of innovations, PhD Thesis, Stanford University.
Durkheim, E., (1982). The Rules of the Sociological Method, London: Macmillan.
Epstein, J and Axtell R., (1996). Growing Artificial Societies: Social Science from the Bottom Up. Cambridge, MA: MIT Press.
Fischer, C.S., (1978). Urban-to-rural diffusion of opinions in contemporary America, American J. of Sociology, 84, p151-159.
Friedkin, N. E., (1980). A Test of Structural Features of Granovetter's Strength of Weak Ties Theory, Social Networks, 2, p411–422.
Gilbert, E., Karahalios, K and Dresden, Y., (2008). The Network in the Garden: An Empirical Analysis of Social Media in Rural Life, Proceedings of CHI, p1603–1612.
Gilbert, E and Karahalois, K., (2009). Predicting tie strengths with social media, J. Computer and Human Interface, 15, p76-97, ACM 978-1-60558-246-7/09/04.
Golbeck, J. (2013). Analyzing the Social Web, Morgan Kaufmann, ISBN: 0-12-405856-6.
Gouldner, A., (1958). Cosmopolitan5 and locals: Toward an analysis of latent social roles, Administrative Science Quarterly 1 and 2, p281.
Granovetter, M., (1973). The Strength of Weak Ties, The American Journal of Sociology, 78(6), p1360–1380.
Granovetter, M. (1978). Threshold Models of Collective Behavior. American J. Sociology, 83(6), p1420–1443. doi:10.1086/226707, (http://dx.doi.org/10.1086%2F226707). JSTOR 2778111
Granovetter, M. (1983). Strength of Weak Ties: A Network Theory Revisited, Sociological Theory, 1, p201–233, doi:10.2307/202051, (//www.jstor.org/stable/202051).
Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness, American J. Sociology, 91(3), p481–510, doi:10.1086/228311.
Handcock, M.S and Gile, K.J., (2009). Modeling social networks from sampled data*, Annals of Applied Statistics, arXiv: math.PR/00000.
Haythornthwaite, C., (2002). Strong, Weak, and Latent Ties and the Impact of New Media, Information Society, 18(5), p385–401.
Haythornthwaite, C and Wellman, B., (1998). Work, Friendship, and Media Use for Information Exchange in a Networked Organization, Journal of American Sociology and Information Science, 49(12), p1101–1114.
Krackhardt, D. (1990). The Strength of Strong Ties: Importance of Philos, In N. Nohria and R. Eccles., Networks and Organizations: Structure, Form and Action (p216–239), Harvard Biz School Press.
Krackhardt, D and Stern, R.N., (1988). Informal Networks and Organizational Crises: Experimental Simulation, Social Psychology Quarterly, 51(2), p123–140.
Kaufman S., (1996). At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. UK: Oxford Univ. Press.
Lin, N., Ensel, W.M and Dayton, P.W., (1981). Social Resources and Strength of Ties: Structural Factors in Occupational Status Attainment, American Sociological Review, 46(4), p393–405.
Macy M and Willer, J., (2002). From factor to actors: computational sociology and agent based model, Annual Review Sociology, 28, p143–166, doi: 10.1146/annurev.soc.28.110601.141117.
Marin, A., (1981). Are Respondents More Likely To List Alters with Certain Characteristics?: Implications for Name Generator Data, Social Networks, 26(4), 289–307.
Marsden, P. V and Campbell, K.E., (1990). Measuring Tie Strength, Social Forces, 63(2), p482–501.
Menzel, H., (1960). Innovation, integration and marginality: a survey of physicians, American Sociological Review, 25, p704-713.
Merton, R.K., (1968). Patterns of influence: Local and cosmopolitan influentials. Reprinted from: P.F. Lazarsfeld and F.N. Stanton (Ed.) 1948-1949. Communication Research. NY: Harper and Brothers.
Michaelson, A.G., (l993). The development of a scientific specialty as diffusion through social relations: The case of role analysis, Social Networks, 15, p217-236.
Mitchell, T.M., (1997). Machine Learning, McGraw Hill publications, ISBN: 0070428077, New Jersey.
Newman, M.E.J., (2003a). Mixing patterns in networks. Physical Review E, 67-026126, p90-102.
Newman, M.E.J, (2003b). The structure and function of complex networks. SIAM Reviews, 45(2), p167-179.
Ojugo, A.A., A.O. Eboka., E.O. Okonta., E.R. Yoro and F.O. Aghware., (2012). Genetic algorithm intrusion detection system, Journal of Emerging Trends in Computing and Information Systems, 3(8): 1182-1194
Ojugo, A.A., (2013). Virus propagation on time varying graphs, Technical-Report, Centre for High Performance and Dynamic Computing, TRON-03-2013-01, Federal University of Petroleum Resources, Nigeria, p24-37.
Ojugo, A.A., (2014). Malware propagation on time varying network: comparative study of machine learning techniques and frameworks, Int. J. Modern Education and Computer Science, 8: 25-33, doi: 10.5815/ijmecs.2014.08.04.
Reynolds C.W., (1987). Flocks, herds, and schools: a distributed behavioral model, Computer Graphics, 21, p25–34
Rogers, E.M., (1983). Diffusion of innovation, 3rd Ed. New York: Free Press.
Rogers, E.M and Kincaid, D.L., (1993). Communication Networks: New Paradigm for Research, NY: Free Press.
Sala, A., Cao, N., Wilson, C., Zablit, R., Zheng, H and Zhao, B.Y., (2010). Measurement calibrated graph models for social network experiments, IW3C2, ACM 978-1-60558-799-8/10/04.
Schnettler, S., (2009). A small world on feet of clay? A comparison of empirical small-world studies against best-practice criteria, Social Networks, 31(3), p179-189, doi:10.1016/j.socnet.2008.12.005.
Scott, J.P., (2000). Social network analysis: A Handbook (2nd Ed). Thousand Oaks, CA: Sage Publications.
Simon H. 1998. The Sciences of the Artificial. Cambridge, MA: MIT Press
Smith T.S and Stevens, G.T., (1999). The architecture of small networks: strong interaction and dynamic organization in small social systems, American Sociological Review, 64, p403–20
Strang, D and Macy, M., (2001). In Search of Excellence:” fads, success stories, and adaptive emulation, American Sociological Review, 107(1), p147.
Toivonen, R., Kovanena, L., Kiveläa, M., Onnela, J.K., Saramäkia, J and Kaskia, K., (2009). A comparative study of social networks models: network evolution and nodal attributes models, Social Networks, 31, p240-254, doi:10.1016/j.socnet.2009.06.004.
Valente, T.W., (1996). Social network thresholds in the diffusion of innovation, Social Networks, 18, p69-89, SSDI 0378-8733(95)00256
Wasserman, S and Faust, K., (1994). Social Network Analysis: Methods and Applications, Cambridge University Press, p1–27, ISBN 97805213870-71.
Weimann, G., (1982). On the importance of marginality: one more step into the two-step flow of communication, American Sociological Review, 47, p764-773.
Wellman, B and Wortley, S., (1990). Different Strokes from Different Folks: Community Ties and Social Support, American Journal of Sociology, 96(3), p558–588.
Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P and Zhao, B.Y., (2009). User interactions in social networks and their implications. In Proc. of EuroSys (April 2009).