A Self-Organizing Map for Identifying InfluentialCommunities in Speech-based Networks

Low-literate people are unable to use many mainstream social networks due to their text-based interfaces even though they constitute a major portion of the world population. Specialized speech-based networks (SBNs) are more accessible to low-literate users through their simple speech-based interfaces. While SBNs have the potential for providing value-adding services to a large segment of society they have been hampered by the need to operate in low-income segments on low budgets. The knowledge of influential users and communities in such networks can help in optimizing their operations. In this paper, we present a self-organizing map (SOM) for discovering and visualizing influential communities of users in SBNs. We demonstrate how a friendship graph is formed from call data records and present a method for estimating influences between users. Subsequently, we develop a SOM to cluster users based on their influence, thus identifying community-level influences and their roles in information propagation. We test our approach on Polly, a SBN developed for job ads dissemination among low-literate users. For comparison, we identify influential users with the benchmark greedy algorithm and relate them to the discovered communities. The results show that influential users are concentrated in influential communities and community-level information propagation provides a ready summary of influential users.