ASRA-GNN: ADAPTIVE SIGNED RELATION-AWARE GRAPH NEURAL NETWORK FOR FRIEND RECOMMENDATION
TL;DR
ASRA-GNN addresses gaps in Signed Graph Neural Networks through three contributions: Sign-Aware Structural Role Attention grounded in four social network theories, a Locally Adaptive Theory Mixing gate replacing TrustSGCN's binary global threshold with a continuous per-node end-toend learned mixing function, and a Signed Contrastive Recommendation Loss providing the first ranking objective for signed user-user graphs.
Abstract
Existing Signed Graph Neural Networks optimize link sign prediction objectives fundamentally misaligned with friend recommendation, while discarding trust asymmetry, edge strength, and adaptive social theory application. We present ASRA-GNN, addressing these gaps through three contributions: Sign-Aware Structural Role Attention (SSRA) grounded in four social network theories; a Locally Adaptive Theory Mixing (LATM) gate replacing TrustSGCN's binary global threshold with a continuous per-node end-toend learned mixing function; and a Signed Contrastive Recommendation Loss providing the first ranking objective for signed user-user graphs using observed positive-negative pairs as natural contrastive anchors. Experiments on Bitcoin-OTC and Bitcoin-Alpha demonstrate an average of Recall@10 of 0.0599, NDCG@10 of 0.4280, and Precision@10 of 0.0840, outperforming all other baselines.