Visualization Through Knowledge Representation Model for Social Networks

Knowledge management is a systematic and organizationally specified process and knowledge management system is all those technological com- ponents; software, hardware, people and processes supporting knowledge management initiative. These initiatives includes work flow maps, web sites, por- tals, document/team management system, data ware- houses, data mining processes, databases, contact lists, virtual teams, collaboration tools, customer re- lationship management, applications and news (Dav- enport and Prusak 1998, Jashapara 2004) (1, 2). Knowledge is not important per se (3) (Agostini et al 2003) instead the process of knowing, learning and creating knowledge is the relevant aspect (4) (Non- aka and Takeuchi 1995). In this paper knowledge representation is presented in 3D style for the under- standing and visualization of dynamics of complex so- cial networks by developing a TANetworkTool (Task Analysis Network Tool). The standard or normal rep- resentation of a typical social network is through a graph data structure in 2D. The dynamics of larger social networks is so complex some time it becomes difficult to understand the various levels of interac- tions and dependencies just by mere representation through a tree or graph. Although, many analyti- cal methods provide relationship dependencies, role of different nodes and their importance in the net- work. In this paper we are presenting a visualization of networks by rotating the network through vari- ous dimensions to provide a more realistic view to understand the dynamics of complex social networks and complimenting the analytical results. This rep- resentation can also help authorities not necessarily having specific scientific background to understand and perhaps take preventive actions required in cer- tain specific scenarios for example dealing with ter- rorist/covert networks.

[1]  H. Leavitt.  Some effects of certain communication patterns on group performance. , 1951, Journal of abnormal psychology.

[2]  Robert L. Burgess,et al.  Communication Networks and Behavioral Consequences! , 1969 .

[3]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[4]  L. Freeman,et al.  Centrality in valued graphs: A measure of betweenness based on network flow , 1991 .

[5]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[6]  D. Rogers,et al.  Sociometric Analysis of Interorganizational Relations; Application of Theory and Measurement. , 1974 .

[7]  J. Nieminen.  On the centrality in a graph. , 1974, Scandinavian journal of psychology.

[8]  野中 郁次郎,et al.  The knowledge-creating company , 2008 .

[9]  I. Nonaka,et al.  The Knowledge Creating Company , 2008 .

[10]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[11]  M. Glanzer,et al.  TECHNIQUES FOR THE STUDY OF TEAM STRUCTURE AND BEHAVIOR PART I: ANALYSIS OF STRUCTURE , 1957 .

[12]  Alex Bavelas,et al.  Communication Patterns in Task‐Oriented Groups , 1950 .

[13]  M. Mulder.  Groepsstructuur, motivatie en prestatie , 1958 .

[14]  Vladimir Batagelj,et al.  Centrality in Social Networks , 1993 .

[15]  TECHNIQUES FOR THE STUDY OF TEAM STRUCTURE AND BEHAVIOR. PART II. EMPIRICAL STUDIES OF THE EFFECTS OF STRUCTURE , 1957 .

[16]  M. Zelen,et al.  Rethinking centrality: Methods and examples☆ , 1989 .

[17]  Alberto Leon-Garcia,et al.  Communication Networks , 2000 .

[18]  Claude Flament,et al.  Applications of Graph Theory to Group Structure , 1966, The Mathematical Gazette.

[19]  Oscar Castillo,et al.  Proceedings of the International MultiConference of Engineers and Computer Scientists 2007, IMECS 2007, March 21-23, 2007, Hong Kong, China , 2007, IMECS.

[20]  Riccardo Dondi,et al.  Stimulating knowledge discovery and sharing , 2003, GROUP.

[21]  Anju Vyas.  Print , 2003 .

[22]  V. Latora,et al.  A measure of centrality based on network efficiency , 2004, cond-mat/0402050.

[23]  Alex Bavelas.  A Mathematical Model for Group Structures , 1948 .

[24]  A. Snadowsky,et al.  Communication Network Research: An Examination of Controversies , 1972 .

[25]  L. H.,et al.  Communication Networks , 1936, Nature.

[26]  M. Glanzer,et al.  Techniques for the study of group structure and behavior. II. Empirical studies of the effects of structure in small groups. , 1961, Psychological bulletin.

[27]  Ashok Jashapara,et al.  Knowledge Management: An Integrated Approach , 2010 .

[28]  F. R. Pitts.  A GRAPH THEORETIC APPROACH TO HISTORICAL GEOGRAPHY , 1965 .

[29]  M. E. Shaw.  Group Structure and the Behavior of Individuals in Small Groups , 1954 .