From Attributes To Communities: a Novel Approach in Social Network Generation

Abstract

Generating networks with attributes would be useful in computer game development by enabling dynamic social interactions, adaptive storylines, realistic economic systems, ecosystem modelling, urban development, strategic planning, and adaptive learning systems. To this end, we propose the Attribute-based Realistic Community and Associate NEtwork (ARCANE) algorithm to generate node-attributed networks with functional communities. We have designed a numerical node attribute-edge relationship computation system to handle the edge generation phase of our network generator, which is a different method from our predecessors. We combine this system with the proximity between nodes to create more life-like communities. Our method is compared against other node-attributed social network generators in the area with using both different evaluation metrics and a real-world dataset. The model properties evaluation identified ARCANE as the leading generator, with another generator ranking in a tie for first place. As a more favorable outcome for our approach, the community detection evaluation indicated that ARCANE exhibited superior performance compared to other competing generators within this domain. This thorough evaluation of the resulting graphs show that the proposed method can be an alternate approach to social network generators with node attributes and communities.