Dual-Channel Feature Fusion for Joint Prediction in Dynamic Signed Weighted Networks

TL;DR

This paper introduces a novel dual-channel feature fusion framework for joint prediction of links, signs, and weights in dynamic signed weighted networks, improving accuracy and reducing error.

Contribution

It proposes a tripartite joint prediction framework with a dual-channel mechanism, integrating semantic embeddings and relational sign features for dynamic network analysis.

Findings

Achieves 2%-4% better accuracy in link and sign prediction.

Reduces edge weight prediction error by 40%-50%.

Effectively models temporal dependencies using a Transformer encoder.

Abstract

Link prediction is central to unraveling social network evolution and node relationships, as well as understanding the characteristic mechanisms of complex networks. Currently, research on link prediction for complex dynamic networks integrating temporal evolution, relational polarity and edge weight information remains significantly underexplored, failing to meet practical demands. For dynamic signed-weighted networks, this paper proposes a tripartite joint prediction framework for unified forecasting of links, signs and weights. First, the dynamic network is decomposed into temporal snapshots, and node semantic embeddings are generated via sign-aware weighted random walks. We then design multi-hop structural balance and temporal difference features to capture the structural characteristics and dynamic evolution laws of the network, respectively. The model adopts a dual-channel feature decoupling mechanism: node semantic embeddings are used for link existence prediction, while relational sign features are fed into a Transformer encoder to model temporal dependencies. Finally, prediction results are output synergistically through a multi-task unit. Simulation experiments demonstrate that, compared with baseline methods, the proposed framework achieves an average 2%-4% improvement in the performance of link existence and relational sign prediction, and a significant 40%-50% reduction in edge weight prediction error.