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.
| Published in | Advances in Networks (Volume 3, Issue 2) |
| DOI | 10.11648/j.net.20150302.11 |
| Page(s) | 8-21 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Stochastic, Immunize, Network, Vertices, SIS, SIR, Function, Search Space, Solution, Models
| [1] | Abraham, A., (2005). Handbook of Measuring System Design, John Wiley and Sons Ltd, ISBN: 0-470-02143-8, pp. 901 – 918. |
| [2] | Alpaydin, E., (2010). Introduction to Machine Learning, McGraw Hill publications, ISBN: 0070428077, NJ |
| [3] | Axelrod R., (1997). The Complexity of Cooperation. Princeton, NJ: Princeton Univ. Press |
| [4] | Beal, G.M and Bolen, J.M., (1955). How farm people accept new ideas, Ames, IA: Cooperative Extension Service Report 15. |
| [5] | Becker, M.H., (1970). Sociometric location and innovativeness: Reformulation and extension of the diffusion model, American Sociological Review, 35, p267-282. |
| [6] | Burt, R.S., (2004). Structural Holes and Good Ideas, American Journal of Sociology, 110(2), p349–399. |
| [7] | Clauset, A., Shalizi, C.R and Newman, M.E.J., (2009). Power-law distributions in empirical data, Siam Review, 51(4), p661–703. |
| [8] | Coleman J.S., (1990). Foundations of Social Theory. Cambridge, MA: Harvard Univ. Press. |
| [9] | David, P.C., (2007). Path dependence – a foundational concept for historical social sciences, Climetrica – Journal of Historical Economics and Econometric History, 1(2). |
| [10] | Davis, J.A., (1961). Locals and cosmopolitans in effects in American graduate schools, Int. Journal of Comparative Sociology, 2(2), p212-223. |
| [11] | Dozier, D.M., (1977). Communication Networks and threshold role in the adoption of innovations, PhD Thesis, Stanford University. |
| [12] | Durkheim, E., (1982). The Rules of the Sociological Method, London: Macmillan. |
| [13] | Epstein, J and Axtell R., (1996). Growing Artificial Societies: Social Science from the Bottom Up. Cambridge, MA: MIT Press. |
| [14] | Fischer, C.S., (1978). Urban-to-rural diffusion of opinions in contemporary America, American J. of Sociology, 84, p151-159. |
| [15] | Friedkin, N. E., (1980). A Test of Structural Features of Granovetter's Strength of Weak Ties Theory, Social Networks, 2, p411–422. |
| [16] | 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. |
| [17] | 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. |
| [18] | Golbeck, J. (2013). Analyzing the Social Web, Morgan Kaufmann, ISBN: 0-12-405856-6. |
| [19] | Gouldner, A., (1958). Cosmopolitan5 and locals: Toward an analysis of latent social roles, Administrative Science Quarterly 1 and 2, p281. |
| [20] | Granovetter, M., (1973). The Strength of Weak Ties, The American Journal of Sociology, 78(6), p1360–1380. |
| [21] | 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 |
| [22] | 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). |
| [23] | Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness, American J. Sociology, 91(3), p481–510, doi:10.1086/228311. |
| [24] | Handcock, M.S and Gile, K.J., (2009). Modeling social networks from sampled data*, Annals of Applied Statistics, arXiv: math.PR/00000. |
| [25] | Haythornthwaite, C., (2002). Strong, Weak, and Latent Ties and the Impact of New Media, Information Society, 18(5), p385–401. |
| [26] | 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. |
| [27] | 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. |
| [28] | Krackhardt, D and Stern, R.N., (1988). Informal Networks and Organizational Crises: Experimental Simulation, Social Psychology Quarterly, 51(2), p123–140. |
| [29] | Kaufman S., (1996). At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. UK: Oxford Univ. Press. |
| [30] | 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. |
| [31] | 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. |
| [32] | Marin, A., (1981). Are Respondents More Likely To List Alters with Certain Characteristics?: Implications for Name Generator Data, Social Networks, 26(4), 289–307. |
| [33] | Marsden, P. V and Campbell, K.E., (1990). Measuring Tie Strength, Social Forces, 63(2), p482–501. |
| [34] | Menzel, H., (1960). Innovation, integration and marginality: a survey of physicians, American Sociological Review, 25, p704-713. |
| [35] | 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. |
| [36] | 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. |
| [37] | Mitchell, T.M., (1997). Machine Learning, McGraw Hill publications, ISBN: 0070428077, New Jersey. |
| [38] | Newman, M.E.J., (2003a). Mixing patterns in networks. Physical Review E, 67-026126, p90-102. |
| [39] | Newman, M.E.J, (2003b). The structure and function of complex networks. SIAM Reviews, 45(2), p167-179. |
| [40] | 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 |
| [41] | 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. |
| [42] | 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. |
| [43] | Reynolds C.W., (1987). Flocks, herds, and schools: a distributed behavioral model, Computer Graphics, 21, p25–34 |
| [44] | Rogers, E.M., (1983). Diffusion of innovation, 3rd Ed. New York: Free Press. |
| [45] | Rogers, E.M and Kincaid, D.L., (1993). Communication Networks: New Paradigm for Research, NY: Free Press. |
| [46] | 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. |
| [47] | 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. |
| [48] | Scott, J.P., (2000). Social network analysis: A Handbook (2nd Ed). Thousand Oaks, CA: Sage Publications. |
| [49] | Simon H. 1998. The Sciences of the Artificial. Cambridge, MA: MIT Press |
| [50] | 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 |
| [51] | Strang, D and Macy, M., (2001). In Search of Excellence:” fads, success stories, and adaptive emulation, American Sociological Review, 107(1), p147. |
| [52] | 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. |
| [53] | Valente, T.W., (1996). Social network thresholds in the diffusion of innovation, Social Networks, 18, p69-89, SSDI 0378-8733(95)00256 |
| [54] | Wasserman, S and Faust, K., (1994). Social Network Analysis: Methods and Applications, Cambridge University Press, p1–27, ISBN 97805213870-71. |
| [55] | Weimann, G., (1982). On the importance of marginality: one more step into the two-step flow of communication, American Sociological Review, 47, p764-773. |
| [56] | Wellman, B and Wortley, S., (1990). Different Strokes from Different Folks: Community Ties and Social Support, American Journal of Sociology, 96(3), p558–588. |
| [57] | 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). |
APA Style
Arnold Adimabua Ojugo, Rume Elizabeth Yoro, Andrew Okonji Eboka, Mary Oluwatoyin Yerokun, Christiana Nneamaka Anujeonye, et al. (2015). Predicting Behavioural Evolution on a Graph-Based Model. Advances in Networks, 3(2), 8-21. https://doi.org/10.11648/j.net.20150302.11
ACS Style
Arnold Adimabua Ojugo; Rume Elizabeth Yoro; Andrew Okonji Eboka; Mary Oluwatoyin Yerokun; Christiana Nneamaka Anujeonye, et al. Predicting Behavioural Evolution on a Graph-Based Model. Adv. Netw. 2015, 3(2), 8-21. doi: 10.11648/j.net.20150302.11
AMA Style
Arnold Adimabua Ojugo, Rume Elizabeth Yoro, Andrew Okonji Eboka, Mary Oluwatoyin Yerokun, Christiana Nneamaka Anujeonye, et al. Predicting Behavioural Evolution on a Graph-Based Model. Adv Netw. 2015;3(2):8-21. doi: 10.11648/j.net.20150302.11
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Download@article{10.11648/j.net.20150302.11,
author = {Arnold Adimabua Ojugo and Rume Elizabeth Yoro and Andrew Okonji Eboka and Mary Oluwatoyin Yerokun and Christiana Nneamaka Anujeonye and Fidelia Ngozi Efozia},
title = {Predicting Behavioural Evolution on a Graph-Based Model},
journal = {Advances in Networks},
volume = {3},
number = {2},
pages = {8-21},
doi = {10.11648/j.net.20150302.11},
url = {https://doi.org/10.11648/j.net.20150302.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.net.20150302.11},
abstract = {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.},
year = {2015}
}
TY - JOUR T1 - Predicting Behavioural Evolution on a Graph-Based Model AU - Arnold Adimabua Ojugo AU - Rume Elizabeth Yoro AU - Andrew Okonji Eboka AU - Mary Oluwatoyin Yerokun AU - Christiana Nneamaka Anujeonye AU - Fidelia Ngozi Efozia Y1 - 2015/08/05 PY - 2015 N1 - https://doi.org/10.11648/j.net.20150302.11 DO - 10.11648/j.net.20150302.11 T2 - Advances in Networks JF - Advances in Networks JO - Advances in Networks SP - 8 EP - 21 PB - Science Publishing Group SN - 2326-9782 UR - https://doi.org/10.11648/j.net.20150302.11 AB - 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. VL - 3 IS - 2 ER -
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