# Explaining Synergistic Effects in Social Recommendations

arXiv: https://arxiv.org/abs/2601.18151 · HTML: https://mlsanity.com/paper/2601.18151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [Reliable Conflictive Multi-View Learning](https://mlsanity.com/paper/2402.16897/) (2024)
- [FairGT: A Fairness-aware Graph Transformer](https://mlsanity.com/paper/2404.17169/) (2024)
- [PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction](https://mlsanity.com/paper/2302.12465/) (2023)
- [Multi-view Information Integration and Propagation for Occluded Person Re-identification](https://mlsanity.com/paper/2311.03828/) (2023)
- [OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining](https://mlsanity.com/paper/2402.15810/) (2024)
- [Reinforced Path Reasoning for Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2207.06674/) (2022)
- [Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks](https://mlsanity.com/paper/2501.16944/) (2025)
- [GNNShap: Scalable and Accurate GNN Explanation using Shapley Values](https://mlsanity.com/paper/2401.04829/) (2024)
- [Long-range Brain Graph Transformer](https://mlsanity.com/paper/2501.01100/) (2025)
- [Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks](https://mlsanity.com/paper/2402.12411/) (2024)
- [A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening](https://mlsanity.com/paper/2406.09291/) (2024)
- [GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning](https://mlsanity.com/paper/2304.06182/) (2023)
- [Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation](https://mlsanity.com/paper/2401.05744/) (2024)
- [MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation](https://mlsanity.com/paper/2405.12519/) (2024)
- [Revisiting Dynamic Graph Clustering via Matrix Factorization](https://mlsanity.com/paper/2502.06117/) (2025)
- [MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation](https://mlsanity.com/paper/2311.16751/) (2023)
- [Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation](https://mlsanity.com/paper/2402.08845/) (2024)
- [FIM: Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation](https://mlsanity.com/paper/2504.17814/) (2025)
- [Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2312.15237/) (2023)
- [DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes](https://mlsanity.com/paper/2508.00664/) (2025)
- [Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization](https://mlsanity.com/paper/2503.20697/) (2025)
- [Globally Interpretable Graph Learning via Distribution Matching](https://mlsanity.com/paper/2306.10447/) (2023)
- [Invariance Matters: Empowering Social Recommendation via Graph Invariant Learning](https://mlsanity.com/paper/2504.10432/) (2025)
- [Explainable Session-based Recommendation via Path Reasoning](https://mlsanity.com/paper/2403.00832/) (2024)
- [Factor Graph-based Interpretable Neural Networks](https://mlsanity.com/paper/2502.14572/) (2025)
- [Higher Order Structures For Graph Explanations](https://mlsanity.com/paper/2406.03253/) (2024)

## Abstract (arXiv)

In social recommenders, the inherent nonlinearity and opacity of synergistic effects across multiple social networks hinders users from understanding how diverse information is leveraged for recommendations, consequently diminishing explainability. However, existing explainers can only identify the topological information in social networks that significantly influences recommendations, failing to further explain the synergistic effects among this information. Inspired by existing findings that synergistic effects enhance mutual information between inputs and predictions to generate information gain, we extend this discovery to graph data. We quantify graph information gain to identify subgraphs embodying synergistic effects. Based on the theoretical insights, we propose SemExplainer, which explains synergistic effects by identifying subgraphs that embody them. SemExplainer first extracts explanatory subgraphs from multi-view social networks to generate preliminary importance explanations for recommendations. A conditional entropy optimization strategy to maximize information gain is developed, thereby further identifying subgraphs that embody synergistic effects from explanatory subgraphs. Finally, SemExplainer searches for paths from users to recommended items within the synergistic subgraphs to generate explanations for the recommendations. Extensive experiments on three datasets demonstrate the superiority of SemExplainer over baseline methods, providing superior explanations of synergistic effects.