Community Algorithm: Classification of users and their roles in a community by their Level of Interaction

Abstract

Analysis of human communities is very much helpful in determining their trends and relationships among community objects. Community can be viewed as a social structure (network). There can be roles of leader or follower in such groups. Communities were defined as nested; i.e. one community can hold another; for example a geographical community may include a number of ethnical communities. Different computer science researchers define community as a graph and as a dense directed bipartite graph, which contains a complete bipartite subgraph of a certain size. Web community is a community of web pages, which can also be defined as “FLG-Communities”. There is a need of algorithm to analyze different human communities, which can deal with quantity of attributes. In this regard, Community Algorithm (CA) is proposed that will be helpful in identifying, analyzing, manipulating, monitoring, and transforming human communities based on human eProfiles. The algorithm is based on three major components that are ontologies, operators and community sticker. Operators are applied to make link between two existing (data) stickers and generating new stickers for individuals added in the community. Community Sticker is comprised over major characteristics of human eProfiles. Human Community Ontology is extended from Community Ontology and holds the definition of the Community Sticker. Profile Ontology categorizes different characteristics of human eProfiles. Three tools (TODE, LiveIT and GAHC) have been developed during the present research, for constructing iontologies and collecting experimental data to facilitate the research during experimentation and its analysis, regarding CA. A community (network) is formed when human eProfiles (nodes) have links (edges) and interactions with each other. If multiple medium of communications were considered like email, chatting and short message service (SMS) in the network, it would make the graph more complex (dense graph or forest). Fuzzy Graphs are utilized for analyzing and modeling levels of information in real-time systems (simple or complex networks). This research analyzes such human communities with the help of fuzzy graphs and highlights the status of individuals in a human community. Max-Min Composition (fuzzy relation) along with statistical analysis on fuzzy graphs of human community was applied, for critical analysis. Two different indices are also proposed and utilized in this research are, Interaction Index (II) and Role Index (RI). Interaction Index (II) was established in order to estimate the intensity of communication in different medium of communication. Similarly Role Index (RI) determines the participation status of individual in a human community. The research envisages towards development of CA, which describes the (computer based) interaction between individuals in a community measurable interaction between users in a community as well as the one between communities. Major attributes of the thesis are:  A novel concept of Community Sticker, having major characteristics of Human eProfile ii Various ontologies holding the background information related to profiles and communities  Multiple operators helping in generating new data stickers for next generation and manipulating data stickers of current generation (community)  Several tools which help in analysis and experimentation of different human communities The thesis begun with Chapter 1 as of literature review to define major attributes of our research.