AIMASIMay 2, 2018

From the Periphery to the Center: Information Brokerage in an Evolving Network

arXiv:1805.00751v17 citations
Originality Incremental advance
AI Analysis

This addresses social integration and status attainment in agent societies, but it is incremental as it builds on existing network theory with new tactics.

The paper tackles the problem of how a newcomer can integrate into a dynamic social network by moving from the periphery to the center using information brokerage, proving that winning tactics exist and demonstrating superior performance with tactics that add very few edges on networks of approximately 14,000 nodes.

Interpersonal ties are pivotal to individual efficacy, status and performance in an agent society. This paper explores three important and interrelated themes in social network theory: the center/periphery partition of the network; network dynamics; and social integration of newcomers. We tackle the question: How would a newcomer harness information brokerage to integrate into a dynamic network going from periphery to center? We model integration as the interplay between the newcomer and the dynamics network and capture information brokerage using a process of relationship building. We analyze theoretical guarantees for the newcomer to reach the center through tactics; proving that a winning tactic always exists for certain types of network dynamics. We then propose three tactics and show their superior performance over alternative methods on four real-world datasets and four network models. In general, our tactics place the newcomer to the center by adding very few new edges on dynamic networks with approximately 14000 nodes.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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