IDEAL 2007

Topological Tree Clustering of Social Network Search Results

By Richard Freeman


In the information age, online collaboration and social networks are of increasing importance and quickly becoming an integral part of our lifestyle. In business, social networking can be a powerful tool to expand a customer network to which a company can sell products and services, or find new
partners / employees in a more trustworthy and targeted manner. Identifying new friends or partners, on social networking websites, is usually done via a keyword search, browsing a directory of topics (e.g. interests, geography, or employer) or a chain of social ties (e.g. links to other friends on a user’s
profile). However there are limitations to these three approaches. Keyword search typically produces a list of ranked results, where traversing pages of ranked results can be tedious and time consuming to explore. A directory of groups / networks is generally created manually, requires significant ongoing
maintenance and cannot keep up with rapid changes. Social chains require the initial users to specify metadata in their profile settings and again may no be up to date. In this paper we propose to use the topological tree method to dynamically identify similar groups based on metadata and content. The topological tree method is used to automatically organise social networking groups. The retrieved results, organised using an online version of the topological tree method, are discussed against to the returned results of a social network search. A discussion is made on the criterions of representing social relationships, and the advantages of presenting underlying topics and providing a clear view of the connections between topics. The topological tree has been found to be a superior representation and well suited for organising social networking content.


Information retrieval, social networking website, social networks, Web 2.0, semantic web, search engine optimization, document clustering, self-organizing maps, topological tree, neural networks, post retrieval clustering, taxonomy generation, enterprise content management, enterprise search,
information management.

Bibliographic Details

   Author = {Freeman, Richard T.},
   Title = {Topological Tree Clustering of Social Network Search Results},
   BookTitle = {Intelligent Data Engineering and Automated Learning-IDEAL 2007. 
   Eight International Conference, 16-19 Dec. 2007},
   Series = {Lecture Notes in Computer Science Vol.4481},
   Address= {Birmingham, UK},
   Publisher = {Springer-Verlag},
   Pages = {760-769},
   Year = {2007}