Learning by Gossip: A Principled Information Exchange Model in Social Networks

TY - JOUR

T1 - Learning by Gossip

T2 - A Principled Information Exchange Model in Social Networks

AU - Apolloni, B.

AU - Malchiodi, D.

AU - Taylor, J. G.

PY - 2013/9

Y1 - 2013/9

N2 - We cope with the key step of bootstrap methods of generating a possibly infinite sequence of random data preserving properties of the distribution law, starting from a primary sample actually drawn from this distribution. We solve this task in a cooperative way within a community of generators where each improves its performance from the analysis of the other partners' production. Since the analysis is based on an a priori distrust of the other partners' production, we denote the partner ensemble as a gossip community and denote the statistical procedure learning by gossip. We prove that this procedure is highly efficient when applied to the elementary problem of reproducing a Bernoulli distribution, with a properly moderated distrust rate when the absence of a long-term memory requires an online estimation of the bootstrap generator parameters. This fact makes the procedure viable as a basic template of an efficient interaction scheme within social network agents.

AB - We cope with the key step of bootstrap methods of generating a possibly infinite sequence of random data preserving properties of the distribution law, starting from a primary sample actually drawn from this distribution. We solve this task in a cooperative way within a community of generators where each improves its performance from the analysis of the other partners' production. Since the analysis is based on an a priori distrust of the other partners' production, we denote the partner ensemble as a gossip community and denote the statistical procedure learning by gossip. We prove that this procedure is highly efficient when applied to the elementary problem of reproducing a Bernoulli distribution, with a properly moderated distrust rate when the absence of a long-term memory requires an online estimation of the bootstrap generator parameters. This fact makes the procedure viable as a basic template of an efficient interaction scheme within social network agents.

KW - Online bootstrap

KW - Neural networks

KW - Learning algorithms

KW - Social networks

U2 - 10.1007/s12559-013-9211-6

DO - 10.1007/s12559-013-9211-6

M3 - Article

SN - 1866-9956

VL - 5

SP - 327

EP - 339

JO - Cognitive computation

JF - Cognitive computation

IS - 3

ER -